CCS:
Genetics

Keywords:
genetics

References:
Dymond, J.S., Scheifele, L.Z., Richardson, S., Lee, P., Chandrasegaran, S., Bader, J.S., and Boeke, J.D. 2009. Teaching synthetic biology, bioinformatics and engineering to undergraduates: the interdisciplinary build-a-genome course. Genetics. 181, 1, 13--21.
Pedersen, J.T., and Moult, J. 1996. Genetic algorithms for protein structure prediction. Current Opinion in Structural Biology. 6, 2, 227--231.

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... Computer Science Education J.3 [Life and Medical Sciences] Biology and genetics; ; General Terms Measurement, Design, Human Factors Keywords Bioinformatics; Genetics 1. INTRODUCTION Why are students in an Introduction to Computer ...
... had taken programming and biology students who had taken molecular genetics [5]. Our course is housed within the computer science department ...
Genetics (BIO) 5 Sequence analysis (BIO) 1 4 Phylogenetic tree construction ... users to donate processing power to solve protein structures [14], genetic algorithms for protein predictions (e.g., [15]) and Fold-it, an online ... ?Comparison of a Highly Polymorphic Olfactory Receptor Gene Subfamily in Genetically Diverse Dog Breeds" [19]. We gave students cheek cell samples ...
... example, ?How to program using python and the basics of genetics. .? Taken together, these results suggest students valued learning computer ...
... computation tools, databases, networks and the Internet, as well as genetics, , basic biochemistry and molecular genetics lab techniques. Students took the course for assorted reasons (Tables ... allow students to compare photographs of the dogs to their genetic data and consider their results in relation to characteristics they ...
... biology, bioinformatics and engineering to undergraduates: the interdisciplinary build-a-genome course. Genetics. . 181, 1, 13-21. [12] Maloney, M., Parker, J., LeBlanc, ... 69, S8, 118-128. [15] Pedersen, J.T., and Moult, J. 1996. Genetic algorithms for protein structure prediction. Current Opinion in Structural Biology. ...

CCS:
Genetic programming

Keywords:
genetic programming
genetic improvement

Title:
Guiding Unconstrained Genetic Improvement

Primary CCS:
Genetic programming

Sponsor:
ACM Special Interest Group on Genetic and Evolutionary Computation

References:
S. Ratcliff, D. R. White, and J. A. Clark. Searching for invariants using genetic programming and mutation testing. In GECCO Proc., 2011.

Full Text:
Guiding Unconstrained Genetic ImprovementDavid R. WhiteUCL, London, UKdavid.r.white@ucl.ac.ukABSTRACTThis paper argues that the potential ... must bebetter guided in order to improve solution quality.KeywordsGenetic Improvement; Genetic Programming; SBSE1. ARBITRARY TRANSFORMATIONSWhat separates GI from other bug-fixing and ... sim-ple template application. Ultimately, this is what makes thefields of Genetic Programming (GP) and GI exciting in theirpotential: they may produce ...
... D. R. White, and J. A. Clark. Searchingfor invariants using genetic programming andmutation testing. In GECCO Proc., 2011.[9] E. K. Smith, ...

CCS:
Genetics

Keywords:
genetic algorithms
genetic code adaptability

Abstract:
<p>The canonical genetic code is almost universal and is present in most complex ... chosen instead of another one. Many researchers believe that the genetic code is a product of natural selection. In this paper, ... paper, we propose a new evaluation function to study the genetic code adaptability. The proposed function uses entropy in order to ... optimize the hypothetical codes to be compared with the canonical genetic code, we use a Genetic Algorithm. The results indicate that the new evaluation function generates ...

Title:
Entropy-based evaluation function for the investigation of genetic code adaptability

References:
F. H. Crick. The origin of the genetic code. Journal of Molecular Biology, 38(3):367--379, 1968.
M. Di Giulio. The extension reached by the minimization of the polarity distances during the evolution of the genetic code. Journal of Molecular evolution, 29(4):288--293, 1989.
S. J. Freeland and L. D. Hurst. The genetic code is one in a million. Journal of Molecular Evolution, 47(3):238--248, 1998.
N. Goldman. Further results on error minimization in the genetic code. Journal of Molecular Evolution, 37(6):662--664, 1993.
D. Haig and L. D. Hurst. A quantitative measure of error minimization in the genetic code. Journal of Molecular Evolution, 33(5):412--417, 1991.
J. Santos and A. Monteagudo. Study of the genetic code adaptability by means of a genetic algorithm. Journal of Theoretical Biology, 264(3):854--865, 2010.
S. Schoenauer and P. Clote. How optimal is the genetic code. In Computer Science and Biology, Proceedings of the German Conference on Bioinformatics (GCB'97), pages 65--67, 1997.
C. R. Woese. On the evolution of the genetic code. Proceedings of the National Academy of Sciences of the United States of America, 54(6):1546--1552, 1965.

Full Text:
Entropy-based evaluation function for the investigation of genetic code adaptabilityEntropy-based evaluation function for the investigation ofgenetic code adaptabilityLariza ... of Computing and MathematicsUniversity of S o PauloRibeir o Preto, Brazilrtinos@ffclrp.usp.brABSTRACTThe canonical genetic code is almost universal and is presentin most complex organisms. ... was chosen instead ofanother one. Many researchers believe that the genetic codeis a product of natural selection. In this paper, we ... this paper, we proposea new evaluation function to study the genetic code adapt-ability. The proposed function uses entropy in order to ... order tooptimize the hypothetical codes to be compared with thecanonical genetic code, we use a Genetic Algorithm. Theresults indicate that the new evaluation function generatesbetter results ... J.3 [Computer Applications]: Lifeand Medical Sciences?Biology and geneticsGeneral TermsAlgorithmsKeywordsGenetic Algorithms, Genetic Code Adaptability1. INTRODUCTIONThe canonical genetic code is almost universal and is presentin most complex organisms. ... the 21 amino acids, there aremore than 1.51 1084 possible genetic codes [7].The reason why the canonical code was selected over ... 7-10, 2012, Orlando, FL, USAACM 978-1-4503-1670-5/12/10Many researchers argue that the genetic code is a productof natural selection, instead of product of ... of product of a random event[1]. The hypothesis that the genetic code has evolved is sup-ported by its robustness against mutations ... typesof encodings can be employed. In the nonrestrictive encod-ing, each genetic code maps the 64 codons into the 21 aminoacids. In ... codons into the 21 aminoacids. In the restrictive encoding, only genetic codes withcodons grouped in the same way as the canonical ... amino acidsassociated to codons.In this way, we propose that the genetic code was adaptednot optimizing only robustness, but optimizing the frequen-cies ... hypothetical ge-netic codes, is implemented using C++ programming lan-guage. The genetic codes are encoded in the GA using thenonrestrictive approach. Each ...
... possible changes toeach base of all codons of a given genetic code C [3], [5],[6], [4]. Polar requirement is generally considered ... for the amino acid codi?ed bythe k-th codon of the genetic code C, and N(i, j, C) is thenumber of possible ... acids withmedium values of polar requirement). In this way, hypothet-ical genetic codes with higher number of codons associatedto the amino acids ... add an entropy-based term to the eval-uation function of the genetic codes. Having more codonscodifying an amino acid in the genetic code, it becomes eas-ier to incorporate an amino acid to ... 1] is a real number and S(C) is theentropy of genetic code C given by:S(C) = ??kp(k, C) log p(k, C) ... is the relative frequency of the k-th aminoacid in the genetic code C. The term Ms(C) is computedusing Eq. 1. It ... uniform asa decreases.2.3 Comparative measurementsIn order to compare the canonical genetic code with hy-pothetical codes generated by the GA in simulations, ... (4)where ?mean is the average ?tness of the possible (ran-dom) genetic codes, ?code is the ?tness of the canon-ical genetic code, and ?low is the ?tness of the bestcode found, ... ?low is the ?tness of the bestcode found, i.e., the genetic code with the lowest ?t-ness found during the optimization process. ... percentage of the best code improvement inrelation to the canonical genetic code ?tness, i.e.,imp = 100 ?code ??low?code(5)The improvement imp should ...
... we propose a new evaluation function forthe study of the genetic code adaptability. In the proposedevaluation function, robustness should be minimized ... to use a re-strictive encoding in order to reduce the genetic code space,as has been done in the literature, but the ... statistical approach (i.e., to compare the canonicalgenetic code with random genetic codes) reinforces that theproposed evaluation function generates better results. Usingthe ... Usingthe proposed evaluation function, for a = 0.7, the canoni-cal genetic code was worse than a smaller number of ran-dom codes ... due to space lim-itation).It is important to observe that hypothetical genetic codeswith better values of the evaluation function, when com-pared to ... values of the evaluation function, when com-pared to the canonical genetic code ?tness, were found bythe GA. In this way, future ... GA. In this way, future work should investigate if thecanonical genetic code represents a local optimum for theproposed evaluation function. Another ... this project.6. REFERENCES[1] F. H. Crick. The origin of the genetic code. Journal ofMolecular Biology, 38(3):367?379, 1968.[2] M. Di Giulio. The ... by theminimization of the polarity distances during theevolution of the genetic code. Journal of Molecularevolution, 29(4):288?293, 1989.[3] S. J. Freeland and ... 29(4):288?293, 1989.[3] S. J. Freeland and L. D. Hurst. The genetic code is onein a million. Journal of Molecular Evolution,47(3):238?248, 1998.[4] ... Evolution,47(3):238?248, 1998.[4] N. Goldman. Further results on error minimization inthe genetic code. Journal of Molecular Evolution,37(6):662?664, 1993.[5] D. Haig and L. ... L. D. Hurst. A quantitative measure oferror minimization in the genetic code. Journal ofMolecular Evolution, 33(5):412?417, 1991.[6] J. Santos and A. ... Monteagudo. Study of the geneticcode adaptability by means of a genetic algorithm.Journal of Theoretical Biology, 264(3):854?865, 2010.[7] S. Schoenauer and P. ... 65?67, 1997.[8] C. R. Woese. On the evolution of the genetic

Title:
Using expert knowledge in initialization for genome-wide analysis of epistasis using genetic programming

CCS:
Genetics

Keywords:
genetic programming
genetic analysis

Abstract:
<p>In human genetics it is now possible to measure large numbers of DNA ... combinations of variations which are predictive of common human diseases. Genetic programming is a promising approach to this problem. The goal ... expert knowledge aware initializer can play in the framework of genetic programming. We show that this expert knowledge aware initializer outperforms ...

Primary CCS:
Genetics

Sponsor:
ACM Special Interest Group on Genetic and Evolutionary Computation

References:
D. E. Goldberg. The Design of Innovation: Lessons from and for Competent Genetic Algorithms. Kluwer Academic Publishers, Norwell, MA, USA, 2002.

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... UntitledUsing Expert Knowledge in Initialization for Genome-wideAnalysis of Epistasis Using Genetic ProgrammingCasey S. GreeneDartmouth CollegeLebanon, NH 03756 USABill C. WhiteDartmouth CollegeLebanon, ... NH 03756 USAJason H. MooreDartmouth CollegeLebanon, NH 03756 USAJason.H.Moore@dartmouth.eduABSTRACTIn human genetics it is now possible to measure large num-bers of DNA ... [Life and Medical Sciences]: biology and geneticsGeneral TermsAlgorithms, PerformanceKeywordsGenetic Analysis, Genetic Programming, Expert Knowledge,Initialization1. INTRODUCTIONIn human genetics it is now possible to measure more thanone million DNA ... from across the hu-man genome. An important goal in human genetics is thedetermination of which of the variations are useful for ... programs are selected, recombined, and mutatedto form new computer programs. Genetic programming andits many variations have been applied successfully to a ...
... ATTRIBUTECONSTRUCTIONMultifactor dimensionality reduction (MDR) was devel-oped as a nonparametric and genetic model-free data miningstrategy for identifying combination of SNPs that are ... have developed a modified ReliefFalgorithm for the domain of human genetics called TunedReliefF (TuRF).5. DATA SIMULATION AND ANALYSISThe goal of the ... to evaluate the powerof GP in the domain of human genetics. . We develop 30 dif-ferent penetrance functions (i.e. genetic models) that definea probabilistic relationship between genotype and pheno-type where ... expert knowledge can pro-vide building blocks necessary to find the genetic needlein the genome-wide haystack. Secondly, expert knowledgeaware initialization performs better ... step closer to routine use of GP strate-gies for the genetic analysis of common human diseases.8. ACKNOWLEDGMENTSThis work was supported by ... E. Goldberg. The Design of Innovation: Lessonsfrom and for Competent Genetic Algorithms. KluwerAcademic Publishers, Norwell, MA, USA, 2002.[2] K. Kira and ...

CCS:
Genetic programming

Keywords:
genetic programming
genetic improvement

Primary CCS:
Genetic programming

References:
Markus F. Brameier and Wolfgang Banzhaf. 2007. Linear Genetic Programming. Springer, New York, NY, USA.
Brendan Cody-Kenny, Edgar Galván López, and Stephen Barrett. 2015. locoGP: Improving Performance by Genetic Programming Java Source Code. In Genetic Improvement 2015 Workshop, William B. Langdon, Justyna Petke, and David R. White (Eds.). ACM, Madrid, Spain, 811--818.
Michael Orlov and Moshe Sipper. 2009. Genetic Programming in the Wild: Evolving Unrestricted Bytecode. In Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, Günther Raidl and others (Eds.). ACM, Montréal Québec, Canada, 1043--1050.
David R. White. 2016. Guiding Unconstrained Genetic Improvement. In Genetic Improvement 2016 Workshop, Justyna Petke, Westley Weimer, and David R. White (Eds.). ACM, Denver, CO, USA, 1133--1134.

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... original language?CCS CONCEPTS? Software and its engineering? Search-based software en-gineering; Genetic programming; Control structures;KEYWORDSGenetic improvement, Java bytecode, genetic programmingACM Reference format:Michael Orlov. 2017. Evolving Software Building Blocks with ... (Scala, Python, Ruby and others).We therefore opt to use linear genetic programming [1] on Javabytecode not due to a preference of ...
... easy to integrate,as opposed to a more involved guidance of genetic improvementprocess, such as proposed by White [6].2 BYTECODE EVOLUTION BACKGROUNDJava ...
... separate examination.REFERENCES[1] Markus F. Brameier and Wolfgang Banzhaf. 2007. Linear Genetic Programming.Springer, New York, NY, USA. https://doi.org/10.1007/978-0-387-31030-5[2] Brendan Cody-Kenny, Edgar Galv n ... Edgar Galv n L pez, and Stephen Barrett. 2015. locoGP:Improving Performance by Genetic Programming Java Source Code. In GeneticImprovement 2015 Workshop, William B. ... Machine. Addison-Wesley,Reading, MA, USA.[4] Michael Orlov and Moshe Sipper. 2009. Genetic Programming in the Wild:Evolving Unrestricted Bytecode. In Proceedings of the ... 2 (April 2011),166?182. https://doi.org/10.1109/TEVC.2010.2052622[6] David R. White. 2016. Guiding Unconstrained Genetic Improvement. In GeneticImprovement 2016 Workshop, Justyna Petke, Westley Weimer, and ...

Title:
Genetic Programming: From Design to Improved Implementation

CCS:
Genetic programming

Keywords:
genetic improvement
genetic programming

Abstract:
<p>Genetic programming (GP) is an evolutionary-based search paradigm that is well ... optimize a particular new implementation of the design, following the Genetic Improvement approach. In particular, this paper presents a case study ... and then the source code is optimized an improved using Genetic Improvement of Software for Multiple Objectives (GISMOE). In the example ...

Primary CCS:
Genetic programming

Sponsor:
ACM Special Interest Group on Genetic and Evolutionary Computation

References:
M. Castelli, L. Trujillo, L. Vanneschi, and A. Popovič. Prediction of energy performance of residential buildings: A genetic programming approach. Energy and Buildings, 102:67--74, 2015.
M. Harman, W. B. Langdon, Y. Jia, D. R. White, A. Arcuri, and J. A. Clark. The gismoe challenge: Constructing the pareto program surface using genetic programming to find better programs (keynote paper), 2012.
J. R. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA, USA, 1992.
J. R. Koza. Human-competitive results produced by genetic programming. Genetic Programming and Evolvable Machines, 11(3-4):251--284, Sept. 2010.
W. B. Langdon. Genetic improvement of software for multiple objectives. In M. de Oliveira Barros and Y. Labiche, editors, Search-Based Software Engineering -- 7th International Symposium, SSBSE 2015, Bergamo, Italy, September 5-7, 2015, Proceedings, volume 9275 of Lecture Notes in Computer Science, pages 12--28. Springer, 2015.
W. B. Langdon and M. Harman. Optimizing existing software with genetic programming. Evolutionary Computation, IEEE Transactions on, 19(1):118--135,Feb 2015.
W. B. Langdon and R. Poli. Foundations of Genetic Programming. Springer Publishing Company, Incorporated, 1st edition, 2010.
T. McConaghy. Genetic Programming Theory and Practice IX, chapter FFX: Fast, Scalable, Deterministic Symbolic Regression Technology, pages 235--260. Springer New York, New York, NY, 2011.
G. Olague and L. Trujillo. Evolutionary-computer-assisted design of image operators that detect interest points using genetic programming. Image Vision Comput., 29(7):484--498, June 2011.
G. Olague and L. Trujillo. Interest point detection through multiobjective genetic programming. Appl. Soft Comput., 12(8):2566--2582, 2012.
J. Petke, W. B. Langdon, and M. Harman. Applying genetic improvement to minisat. In G. Ruhe and Y. Zhang, editors, SSBSE, volume 8084 of Lecture Notes in Computer Science, pages 257--262. Springer, 2013.
S. Silva and J. Almeida. Gplab-a genetic programming toolbox for matlab. In Proc. of the Nordic MATLAB Conference (NMC-2003, pages 273--278, 2005.
L. Spector. Automatic Quantum Computer Programming: A Genetic Programming Approach (Genetic Programming). Springer-Verlag New York, Inc., Secaucus, NJ, USA, 2006.
L. Trujillo, P. Legrand, G. Olague, and J. LéVy-VéHel. Evolving estimators of the pointwise hölder exponent with genetic programming. Inf. Sci., 209:61--79, Nov. 2012.
L. Vanneschi, M. Castelli, and S. Silva. A survey of semantic methods in genetic programming. Genetic Programming and Evolvable Machines, 15(2):195--214, June 2014.

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Genetic Programming: From Design to ImprovedImplementationV ctor R. L pez-L pez,Leonardo TrujilloInstituto Tecnol gico deTijuanaTijuana, ... used to optimizea particular new implementation of the design, followingthe Genetic Improvement approach. In particular, this pa-per presents a case study ... OpenCV, and then the sourcecode is optimized an improved using Genetic Improvementof Software for Multiple Objectives (GISMOE). In the exam-ple we ... the software develop-ment process, from design to improved implementation.KeywordsGenetic programming; genetic ... improvement; computer vi-sion1. INTRODUCTIONOver the past twenty years or so, Genetic ProgrammingPublication rights licensed to ACM. ACM acknowledges that this contribution ... the Fast Function Extraction al-gorithm (FFX) [11] and Geometric Semantic Genetic Pro-gramming (GSGP) [24]. These algorithms offer powerfulmodeling techniques that are ... GP to refer tothe most common and original variant of genetic program-ming, as proposed by John Koza, using a tree representationand ...
... to address this kind of task, in what isknown as Genetic Improvement (GI) [9, 2, 8, 15, 25], al-lowing us to ...
... provided, which is the core algorithm used to derive theoperator.3.1 Genetic ProgrammingEvolutionary algorithms (EA) are population-basedsearch methods, where candidate solutions are ... [7]. In the present work we havechosen to use the Genetic Improvement of Software for Mul-tiple Objective Exploration (GISMOE) approach and ...
... Vanneschi, and A. Popovic?.Prediction of energy performance of residentialbuildings: A genetic programming approach. Energyand Buildings, 102:67 ? 74, 2015.[2] M. Harman, ... technology 5 mission. Evol. Comput.,19(1):1?23, Mar. 2011.[5] J. R. Koza. Genetic Programming: On theProgramming of Computers by Means of NaturalSelection. MIT ... USA, 1992.[6] J. R. Koza. Human-competitive results produced bygenetic programming. Genetic Programming andEvolvable Machines, 11(3-4):251?284, Sept. 2010.[7] W. B. Langdon. Genetic improvement of software formultiple objectives. In M. de Oliveira Barros ... 1?8,2010.[9] W. B. Langdon and M. Harman. Optimizing existingsoftware with genetic programming. EvolutionaryComputation, IEEE Transactions on, 19(1):118?135,Feb 2015.[10] W. B. Langdon ... of GeneticProgramming. Springer Publishing Company,Incorporated, 1st edition, 2010.[11] T. McConaghy. Genetic Programming Theory andPractice IX, chapter FFX: Fast, Scalable,Deterministic Symbolic Regression ... 2011.[14] G. Olague and L. Trujillo. Interest point detectionthrough multiobjective genetic programming. Appl.Soft Comput., 12(8):2566?2582, 2012.[15] M. Orlov and M. Sipper. ... IEEE Computer Society, 1994.[18] S. Silva and J. Almeida. Gplab-a genetic programmingtoolbox for matlab. In In Proc. of the NordicMATLAB Conference ... Conference (NMC-2003, pages 273?278,2005.[19] L. Spector. Automatic Quantum ComputerProgramming: A Genetic Programming Approach(Genetic Programming). Springer-Verlag New York,Inc., Secaucus, NJ, USA, 2006.[20] L. Trujillo, ... Olague, andJ. Le Vy-Ve Hel. Evolving estimators of the pointwiseho lder exponent with genetic programming. Inf. Sci.,209:61?79, Nov. 2012.[21] L. Trujillo and G. Olague. ... M. Castelli, and S. Silva. A survey ofsemantic methods in genetic programming. GeneticProgramming and Evolvable Machines, 15(2):195?214,June 2014.[25] D. White, A. ...

CCS:
Genetics

Keywords:
genetics

Primary CCS:
Genetics

Sponsor:
ACM Special Interest Group on Genetic and Evolutionary Computation

References:
C. S. Greene, B. C. White, and J. H. Moore. Ant colony optimization for genome-wide genetic analysis. In ANTS '08: Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence, pages 37--47, Berlin, Heidelberg, 2008. Springer-Verlag.
J. H. Moore, J. C. Gilbert, C. T. Tsai, F. T. Chiang, T. Holden, N. Barney, and B. C. White. A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility. Journal of Theoretical Biology, 241(2):252--261, Jul 2006.
M. D. Ritchie, L. W. Hahn, N. Roodi, L. R. Bailey, W. D. Dupont, F. F. Parl, and J. H. Moore. Multifactor dimensionality reduction reveals high-order interactions among estrogen metabolism genes in sporadic breast cancer. American Journal of Human Genetics, 69:138--147, 2001.
N. Sinnott-Armstrong, C. Greene, F. Cancare, and J. Moore. Accelerating epistasis analysis in human genetics with consumer graphics hardware. BMC Research Notes, 2(1):149, 2009.

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... and Subject Descriptors: J.3 [Life and Med-ical Sciences]: biology and genetics, , healthGeneral Terms: Algorithms, PerformanceKeywords: ACO, GPU, Genetics1. INTRODUCTIONWith advances ...
... C. White, and J. H. Moore. Ant colonyoptimization for genome-wide genetic analysis. In ANTS ?08:Proceedings of the 6th international conference on ... computationalframework for detecting, characterizing, and interpretingstatistical patterns of epistasis in genetic studies of humandisease susceptibility. Journal of Theoretical Biology,241(2):252?261, Jul 2006.[7] ... among estrogenmetabolism genes in sporadic breast cancer. American Journalof Human Genetics, , 69:138?147, 2001.[8] M. Schatz, C. Trapnell, A. Delcher, and ... Greene, F. Cancare, and J. Moore.Accelerating epistasis analysis in human genetics

CCS:
Genetic algorithms

Keywords:
genetic algorithm

Abstract:
... generate models to retrace the underlying dynamics, we deploy a Genetic Algorithm (GA). The GA optimises the parameters of physics-based virtual ... volumetric data with virtual cells, and the configuration of the Genetic

Primary CCS:
Genetic algorithms

Sponsor:
ACM Special Interest Group on Genetic and Evolutionary Computation

Full Text:
... to generate models toretrace the underlying dynamics, we deploy a Genetic Algorithm(GA). e GA optimises the parameters of physics-based virtualcells to ... imported volumetric data withvirtual cells, and the con guration of the Genetic Algorithm.CCS CONCEPTS?Computingmethodologies?Genetic algorithms; ?Appliedcomputing? Computational biology;KEYWORDSGenetic Algorithm, Developmental Biology1 INTRODUCTIONIn this paper, ... the CT-scan to initialisea virtual cell population and utilise a Genetic Algorithm (GA) tooptimise the model?s parameters. BOODLE not only provides ...
... populated the initialsurfaces with two types of virtual cells. e genetic algorithm wascon gured to feature a population of ten solutions of ... average performing phenotype, yielding a total tness values of 0.62.information. A genetic algorithm was used to optimise cell be-haviour on annotated CT-data ... of optimization.Once the full processing loop for parametric optimisation basedon genetic algorithms is set up, genetic programming techniquesmay help in more aptly capturing the complex facets ...

CCS:
Genetic programming

Keywords:
genetic programming

Abstract:
... method based on back-propagation algorithm known from neural networks. Multi-Gene Genetic Programming (MGGP) was chosen as a baseline model. As a ...

Primary CCS:
Genetic programming

Sponsor:
ACM Special Interest Group on Genetic and Evolutionary Computation

References:
Ignacio Arnaldo, Krzysztof Krawiec, and Una-May O'Reilly. 2014. Multiple Regression Genetic Programming. In Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation (GECCO '14). ACM, New York, NY, USA, 879--886.
Ignacio Arnaldo, Una-May O'Reilly, and Kalyan Veeramachaneni. 2015. Building Predictive Models via Feature Synthesis. In Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation (GECCO '15). ACM, New York, NY, USA, 983--990.
Mark Hinchliffe, Hugo Hiden, Ben McKay, Mark Willis, Ming Tham, and Geoffery Barton. 1996. Modelling Chemical Process Systems Using a Multi-Gene Genetic Programming Algorithm. In Late Breaking Paper, GP'96. Stanford, USA, 56--65.
John R. Koza. 1992. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA, USA. http://mitpress.mit.edu/books/genetic-programming
Trent McConaghy. 2011. FFX: Fast, Scalable, Deterministic Symbolic Regression Technology. In Genetic Programming Theory and Practice IX, Rick Riolo, Ekaterina Vladislavleva, and Jason H. Moore (Eds.). Springer New York, 235--260.

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... themin only a single case.CCS CONCEPTS?Computingmethodologies? Supervised learning by regres-sion; Genetic programming;KEYWORDSgenetic programming, symbolic regressionACM Reference format:Jan Z?egklitz and Petr Pos?? k. ...
... appreciated.REFERENCES[1] Ignacio Arnaldo, Krzysztof Krawiec, and Una-May O?Reilly. 2014. MultipleRegression Genetic Programming. In Proceedings of the 2014 Annual Conferenceon Genetic and Evolutionary Computation (GECCO ?14). ACM, New York, NY,USA, 879?886. ... via Feature Synthesis. In Proceedings of the 2015 AnnualConference on Genetic and Evolutionary Computation (GECCO ?15). ACM, NewYork, NY, USA, 983?990. ... (NC 2000), Vol. 2000. Citeseer, 115?121.[5] John R. Koza. 1992. Genetic Programming: On the Programming of Computers byMeans of Natural Selection. ...
Scalable, Deterministic Symbolic RegressionTechnology. In Genetic Programming eory and Practice IX, Rick Riolo, EkaterinaVladislavleva, and Jason H. ...

Title:
Environmental noise improves epistasis models of genetic data discovered using a computational evolution system

CCS:
Genetics

Keywords:
genetics

Abstract:
... interactions between multiple DNA sequence variations. One goal of human genetics is to use data mining and machine learning methods to ... data mining and machine learning methods to identify combinations of genetic variations that are predictive of discrete measures of health in ...

Primary CCS:
Genetics

Sponsor:
ACM Special Interest Group on Genetic and Evolutionary Computation

References:
W. Banzhaf, G. Beslon, S. Christensen, J. A. Foster, F. Kepes, V. Lefort, J. Miller, M. Radman, and J. J. Ramsden. From artificial evolution to computational evolution: a research agenda. Nature Reviews Genetics, 7:729--735, 2006.
J. Moore, P. Andrews, N. Barney, and B. White. Development and evaluation of an open-ended computational evolution system for the genetic analysis of susceptibility to common human diseases. Lecture Notes in Computer Science, 4973:129--140, 2008.
J. H. Moore, C. S. Greene, P. C. Andrews, and B. C. White. Does complexity matter? artificial evolution, computational evolution and the genetic analysis of epistasis in common human diseases. In Genetic Programming Theory and Practice VI, pages 125--143. Springer, 2009.
J. H. Moore, J. S. Parker, N. J. Olsen, and T. Aune. Symbolic discriminant analysis of microarray data in autoimmune disease. Genetic Epidemiology, 23:57--69, 2002.
J. H. Moore and B. C. White. Genome--wide genetic analysis using genetic programming: The critical need for expert knowledge. Genetic Programming Theory and Practice IV, 2007.

Full Text:
Environmental noise improves epistasis models of genetic data discovered using a computational evolution systemEnvironmental Noise Improves Epistasis ... nonlinear inter-actions between multiple DNA sequence variations. Onegoal of human genetics is to use data mining and machinelearning methods to identify ... use data mining and machinelearning methods to identify combinations of genetic vari-ations that are predictive of discrete measures of health inhuman ... noisy.Categories and Subject DescriptorsJ.3 [Life and Medical Sciences]: biology and genetics, ... ,healthGeneral TermsAlgorithms, PerformanceKeywordsComputational Evolution, Noise, Genetics1. INTRODUCTIONThe practice of human genetics is rapidly changing due tothe availability of new technologies that ... interactions. Epistasis isbelieved to be a ubiquitous component of the genetic archi-tecture of common human diseases [7]. Previously Moore etal. [6, ... strat-egy capable of detecting and characterizing gene-gene in-teractions in human genetics. . The goal of this study is toexamine whether the ... open-ended evolution for bioinformatics problemsolving in the domain of human genetics. . This framework ishierarchically organized and described in detail in ...
... B. C.White. Does complexity matter? artificial evolution,computational evolution and the genetic analysis ofepistasis in common human diseases. In GeneticProgramming Theory and ... and T. Aune.Symbolic discriminant analysis of microarray data inautoimmune disease. Genetic Epidemiology, 23:57?69,2002.[11] J. H. Moore and B. C. White. Genome-wide ... J. H. Moore and B. C. White. Genome-wide geneticanalysis using genetic programming: The critical needfor expert knowledge. Genetic Programming Theoryand Practice IV, 2007.[12] A. Wagner. Robustness and Evolvability ...

CCS:
Genetics

Keywords:
Biology and genetics, Optimization

Primary CCS:
Genetics

References:
R.H. Crozier, "Genetic Diversity and the Agony of Choice," Biological Conservation, vol. 61, pp. 11-15, 1992.
R.H. Crozier, "Preserving the Information Content of Species: Genetic Diversity, Phylogeny, and Conservation Worth," Ann. Rev. Ecology and Systematics, vol. 28, pp. 243-268, 1997.
F. Pardi and N. Goldman, "Species Choice for Comparative Genomics: Being Greedy Works," PLoS Genetics, vol. 1, pp. 672-675, 2005.
M. Nei, Molecular Evolutionary Genetics. Columbia Univ. Press, 1987.

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... only the treetopology but also evolutionary distances. Other measuressuch as genetic diversity [6] also use phylogenetic trees astheir basis and are ...
... optimal PD set [18]. ANeighbor-net can also be constructed from genetic distancesbetween molecular sequences and the PD-NET algorithmcan be applied. An ...
... Phylogenetic Diversity,?Biological Conservation, vol. 61, pp. 1-10, 1992.[6] R.H. Crozier, ?Genetic Diversity and the Agony of Choice,?Biological Conservation, vol. 61, pp. ... N. Goldman, ?Species Choice for ComparativeGenomics: Being Greedy Works,? PLoS Genetics, , vol. 1,pp. 672-675, 2005.[11] B.Q. Minh, S. Klaere, and ...
Nei, Molecular Evolutionary Genetics. . Columbia Univ. Press,1987.[18] B.Q. Minh, S. Klaere, and A. ... St. Catharine?sCollege, University of Cambridge. His researchinterests include comparative genomics, phylo-genetics, , and in particular the algorithmic as-pects of these disciplines.Steffen ... bioinformatics. His research interests are phylogenetictree reconstruction, modeling evolution, population genetics, , algorithmsfor bioinformatics, and biodiversity.. For more information on this ...

CCS:
Genetics

Keywords:
genetics

Primary CCS:
Genetics

CCS:
Genetics

Keywords:
Applications, Biology and genetics

References:
H.-J. Bandelt, P. Forster, B.C. Sykes, and M.B. Richards, "Mitochondrial Portraits of Human Populations Using Median Networks," Genetics, vol. 141, pp. 743-753, 1995.
F. Delsuc, H. Brinkmann, and H. Philippe, "Phylogenomics and the Reconstruction of the Tree of Life," Nature Rev. Genetics, vol. 6, pp. 361-375, 2005.

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... Sykes, and M.B. Richards,?Mitochondrial Portraits of Human Populations Using MedianNetworks,? Genetics, , vol. 141, pp. 743-753, 1995.[3] H.-J. Bandelt, Median Hulls ... ?Phylogenomics andthe Reconstruction of the Tree of Life,? Nature Rev. Genetics, , vol. 6,pp. 361-375, 2005.[8] J. Felsenstein, Inferring Phylogenies. Sinauer ...

CCS:
Genetics

Keywords:
population genetics
last genetic contact

Abstract:
<p>The rapidly growing amount of ancient human genetic data enables the tracking of the spread of human populations ... data. We introduce an efficient algorithm that generates a last genetic contact tree for a set of populations. The computation complexity ...

Primary CCS:
Genetics

Title:
A Last Genetic Contact Tree Generation Algorithm for a Set of Human Populations

Title:
Mask functions for the symbolic modeling of epistasis using genetic programming

CCS:
Genetics

Keywords:
genetic programming
genetic analysis
genetic mask
genetic epidemiology

Abstract:
<p>The study of common, complex multifactorial diseases in genetic epidemiology is complicated by nonlinearity in the genotype-to-phenotype mapping relationship ... discriminant analysis (SDA) is a flexible modeling approach which uses genetic programming (GP) to evolve an optimal predictive model using a ... for modeling epistasis. In the present study, we introduce the genetic .mask. as a novel building block which exploits expert knowledge ...

Primary CCS:
Genetics

Sponsor:
ACM Special Interest Group on Genetic and Evolutionary Computation

References:
W. Banzhaf, P. Nordin, R. E. Keller, and F. D. Francone. Genetic programming: an introduction: on the automatic evolution of computer programs and its applications. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1998.]]
C. Greene, B. White, and J. Moore. An Expert Knowledge-Guided Mutation Operator for Genome-Wide Genetic Analysis Using Genetic Programming.]]
J. KOZA. The Genetic Programming Paradigm: Genetically Breeding Populations of Computer Programs to Solve Problems. Dynamic, Genetic, and Chaotic Programming: The Sixth-Generation, 1992.]]
J. R. Koza. Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge, MA, USA, 1992.]]
J. R. Koza. Genetic programming II: automatic discovery of reusable programs. MIT Press, Cambridge, MA, USA, 1994.]]
J. R. Koza. Genetic Programming IV: Routine Human-Competitive Machine Intelligence. Kluwer Academic Publishers, Norwell, MA, USA, 2003.]]
J. R. Koza, D. Andre, F. H. Bennett, and M. A. Keane. Genetic Programming III: Darwinian Invention & Problem Solving. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1999.]]
W. B. Langdon and R. Poli. Foundations of Genetic Programming. Springer--Verlag, 2002.]]
W. B. Langdon and K. J. R. Genetic Programming and Data Structures: Genetic Programming + Data Structures = Automatic Programming! Kluwer Academic Publishers, Norwell, MA, USA, 1998.]]
R. Lipshutz, S. Fodor, T. Gingeras, D. Lockhart, et al. High density synthetic oligonucleotide arrays. Nature Genetics, 21(Suppl 1):20--24, 1999.]]
J. Moore. Cross Validation Consistency for the Assessment of Genetic Programming Results in Microarray Studies. Applications of Evolutionary Computing: EvoWorkshops 2003: EvoBIO, EvoCOP, EvoIASP, EvoMUSART, EvoROB, and EvoSTIM, Essex, UK, April 14--16, 2003: Proceedings, 2003.]]
J. Moore. Genome-wide analysis of epistasis using multifactor dimensionality reduction: feature selection and construction in the domain of human genetics. Knowledge Discovery and Data Mining: Challenges and Realities with Real World Data, 2006.]]
J. Moore, N. Barney, B. White, R. Riolo, T. Soule, and B. Worzel. Solving Complex Problems In Human Genetics Using. Genetic Programming Theory and Practice {V}, pages 69--86.]]
J. Moore, J. Gilbert, C. Tsai, F. Chiang, T. Holden, N. Barney, and B. White. A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility. Journal of Theoretical Biology, 241(2):252--261, 2006.]]
J. Moore, J. Parker, N. Olsen, and T. Aune. Symbolic discriminant analysis of microarray data in autoimmune disease. Genetic Epidemiology, 23(1):57--69, 2002.]]
J. Moore and B. White. Exploiting expert knowledge in genetic programming for genome-wide genetic analysis. Lecture Notes in Computer Science, 4193:969--977, 2006.]]
J. Moore and B. White. Genome-wide genetic analysis using genetic programming: The critical need for expert knowledge. Genetic Programming Theory and Practice IV. New York, Springer, 2006.]]
R. Neuman and J. Rice. Two-locus models of disease. Genet Epidemiol, 9(5):347--65, 1992.]]
M. Ritchie, L. Hahn, N. Roodi, L. Bailey, W. Dupont, F. Parl, and J. Moore. Multifactor-Dimensionality Reduction Reveals High-Order Interactions among Estrogen-Metabolism Genes in Sporadic Breast Cancer. The American Journal of Human Genetics, 69(1):138---147, 2001.]]

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Mask Functions for the Symbolic Modeling of EpistasisUsing Genetic ProgrammingRyan J. UrbanowiczDartmouth College1 Medical Center Dr.Hanover, NH 03755,USABill C. ... Symobolic dis-criminant analysis (SDA) is a flexible modeling approachwhich uses genetic programming (GP) to evolve an opti-mal predictive model using a ... strategy for modeling epistasis.In the present study, we introduce the genetic ?mask? asa novel building block which exploits expert knowledge inthe ... and Medical Sciences?biology and geneticsGeneral TermsAlgorithms, Design, Human FactorsKeywordsGenetic Analysis, Genetic Epidemiology, Genetic ... Program-ming, Symbolic Discriminant Analysis, Symbolic Regres-sion, Function Set, Two-Locus Model, Genetic Mask1. INTRODUCTIONAdvancing laboratory techniques such as DNA microar-rays [30] and ... and computational methods utilized to interpretthis information. The challenge for genetic epidemiologistsPermission to make digital or hard copies of all or ... statistical and computational methodsthat are able to identify subsets of genetic attributes thatclassify and predict clinical endpoints. In the 1930?s, SirRonald ...
onlythe models which reduce the genetic redundancy present inentire set, (2) selecting models believed to be ... building block selection can improve themodeling of epistatic interactions.2. METHODS2.1 Genetic MasksThe idea behind a ?mask? is to provide SDA with ... geneticist might view a?mask?as being a disease model involving two genetic loci. As dis-cussed by Li et al. [12], these two-locus ... Penetrance is defined as theprobability of disease given a particular genetic state.In the most general case, the penetrance (fij) of a ...
... availability of these different maskfunctions improves SDA modeling of complex genetic rela-tionships.2.2 Mask Selection by Multifactoral Dimen-sionality Reduction (MDR)A potentially superior ...
... it has a statistically significant marginalor independent main effect [25].3412.4 Genetic ProgrammingGenetic programming is an automated computational dis-covery tool that is ...
... to op-timize search parameters, (2) carrying out a coarse-grainedsearch using genetic programming (GP), (3) generating ex-pert knowledge by statistically modeling the ... step as a rapid evaluation of masks for solvingthe complex genetic modeling problem with and without theavailability of a mask function ...
... case of M 96 and M 48it seems that valuable genetic interaction relationships werepreserved after reducing the redundancy present from all512 ... potential advantage of masks extendsmuch further. We hypothesis that utilizing genetic masksas model building blocks will additionally hasten and sim-plify the ... W. Banzhaf, P. Nordin, R. E. Keller, and F. D.Francone. Genetic programming: an introduction: onthe automatic evolution of computer programs and ... B. White, and J. Moore. An ExpertKnowledge-Guided Mutation Operator forGenome-Wide Genetic Analysis Using GeneticProgramming.[4] L. Hahn, M. Ritchie, and J. Moore. ... software for detectinggene-gene and gene-environment interactions, 2003.[5] J. Koza. The Genetic ... Programming Paradigm:Genetically Breeding Populations of ComputerPrograms to Solve Problems. Dynamic, Genetic, , andChaotic Programming: The Sixth-Generation, 1992.345[6] J. R. Koza. Genetic programming: on theprogramming of computers by means of naturalselection. MIT ... naturalselection. MIT Press, Cambridge, MA, USA, 1992.[7] J. R. Koza. Genetic programming II: automaticdiscovery of reusable programs. MIT Press,Cambridge, MA, USA, ... reusable programs. MIT Press,Cambridge, MA, USA, 1994.[8] J. R. Koza. Genetic Programming IV: RoutineHuman-Competitive Machine Intelligence. KluwerAcademic Publishers, Norwell, MA, USA, ... R. Koza, D. Andre, F. H. Bennett, and M. A.Keane. Genetic
... GeneticProgramming. Springer-Verlag, 2002.[11] W. B. Langdon and K. J. R. Genetic Programmingand Data Structures: Genetic Programming + DataStructures = Automatic Programming! KluwerAcademic Publishers, Norwell, MA, ... 1):20?24, 1999.[14] J. Moore. Cross Validation Consistency for theAssessment of Genetic Programming Results inMicroarray Studies. Applications of EvolutionaryComputing: EvoWorkshops 2003: EvoBIO, ... T. Soule,and B. Worzel. Solving Complex Problems In HumanGenetics Using. Genetic Programming Theory andPractice {V}, pages 69?86.[19] J. Moore, J. Gilbert, ... and T. Aune. Symbolicdiscriminant analysis of microarray data inautoimmune disease. Genetic ... Epidemiology,23(1):57?69, 2002.[22] J. Moore and B. White. Exploiting expert knowledgein genetic programming for genome-wide geneticanalysis. Lecture Notes in Computer Science,4193:969?977, 2006.[23] ... in Computer Science,4193:969?977, 2006.[23] J. Moore and B. White. Genome-wide genetic analysisusing genetic programming: The critical need forexpert knowledge. Genetic Programming Theory andPractice IV. New York, Springer, 2006.[24] J. H. ... amongEstrogen-Metabolism Genes in Sporadic BreastCancer. The American Journal of Human Genetics, ,69(1):138?147, 2001.[29] J. Rowland. Model selection methodology insupervised learning with ...

CCS:
Genetics

Keywords:
Algorithms, Biology and genetics

References:
M.J. Daly, J.D. Rioux, S.F. Schaffner, T.J. Hudson, and E.S. Lander, "High-Resolution Haplotype Structure in the Human Genome," Nature Genetics, vol. 29, pp. 229-232, 2001.
X. Ke, S. Hunt, W. Tapper, R. Lawrence, G. Stavrides, J. Ghori, P. Whittaker, A. Collins, A.P. Morris, D. Bentley, L.R. Cardon, and P. Deloukas, "The Impact of SNP Density on Fine-Scale Patterns of Linkage Disequilibrium," Human Molecular Genetics, vol. 13, no. 6, pp. 577-588, 2004.
J.K. Pritchard and M. Przeworski, "Linkage Disequilibrium in Humans: Models and Data," Am. J. Human Genetics, vol. 69, pp. 1-14, 2001.

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... the goals of current research trends, and in this area,new genetic diagnostic methods are critical. It is thusimportant to support diagnostic ...
... andbound (BNB) approach, while the second algorithm is basedon a genetic approach (GA). They show that GA producesreconstructions of quality comparable ...
... Hudson, and E.S. Lander,?High-Resolution Haplotype Structure in the Human Genome,?Nature Genetics, , vol. 29, pp. 229-232, 2001.[8] R. Grossi, A. Gupta, ... of SNP Density on Fine-ScalePatterns of Linkage Disequilibrium,? Human Molecular Genetics, ,vol. 13, no. 6, pp. 577-588, 2004.[13] G. Lancia, V. ...
... Przeworski, ?Linkage Disequilibrium inHumans: Models and Data,? Am. J. Human Genetics, , vol. 69,pp. 1-14, 2001.[22] R. Sachidanandam et al., ?A ...

Title:
Primer design for multiplex PCR using a genetic algorithm

CCS:
Genetics

Keywords:
genetic algorithm

Abstract:
... we propose a multiplex PCR primer design tool using a genetic algorithm, which is a stochastic approach based on the concept ... stochastic approach based on the concept of biological evolution, biological genetics and genetic operations on chromosomes, to find an optimal selection of primer ...

Primary CCS:
Genetics

Sponsor:
ACM Special Interest Group on Genetic and Evolutionary Computation

References:
Davis, L., Genetic algorithms and simulated annealing, in 1. 1987.
Goldberg, D.E., Genetic Algorithms in Search, Optimization, and Machine Learning. 1989, New York: Addison-Wesley.

Full Text:
... Microsoft Word - p196-horng2.docPrimer Design for Multiplex PCR Using a Genetic Algorithm Feng-Mao Lin1 meta@db.csie.ncu.edu.tw Hsien-Da Huang2 bryan@mail.nctu.edu.tw His-Yuan Huang2 aliken.bi93g@nctu.edu.tw ... we propose a multiplex PCR primer design tool using a genetic algorithm, which is a stochastic approach based on the concept ... stochastic approach based on the concept of biological evolution, biological genetics and genetic operations on chromosomes, to find an optimal selection of primer ... J.3 [Computer Application]: Life and medical sciences ? biology and genetics. . General Terms Algorithms, Design Keywords Genetic algorithm, Multiplex PCR, Primer 1. INTRODUCTION Polymerase chain reaction (PCR) ... mix-up during reaction setup. In this paper, we use the genetic algorithm (GA) to design primers for multiplex PCR. Genetic algorithms were formally introduced in the United States in the ... by John Holland at University of Michigan. He described the ?genetic algorithm?, as a control structure with representations and operations that ... strings that were adapted to the problem to be solved. Genetic algorithms tend to converge on solutions that are globally optimal ... PCR primer design. 2.2 Multiplex PCR Primer Design Using a Genetic Algorithm Each chromosome is one of the solutions of the ... process flow is based on the architecture of a simple genetic algorithm [2]. The following are termination conditions: The number of ...
... to the area of specificity. 5. References [1] Davis, L., Genetic ... algorithms and simulated annealing, in 1. 1987. [2] Goldberg, D.E., Genetic Algorithms in Search, Optimization, and Machine Learning. 1989, New York: ...

CCS:
Genetic programming

Keywords:
genetic programming

Abstract:
<p>The PushGP genetic programming system, which evolves programs expressed in the Push programming ...

Primary CCS:
Genetic programming

References:
Thomas Helmuth. 2015. General Program Synthesis from Examples Using Genetic Programming with Parent Selection Based on Random Lexicographic Orderings of Test Cases. Ph.D. dissertation. http://scholarworks.umass.edu/dissertations_2/465/
Thomas Helmuth, Nicholas Freitag McPhee, Edward Pantridge, and Lee Spector. 2017. Improving Generalization of Evolved Programs through Automatic Simplification. In Proceedings of the Genetic and Evolutionary Computation Conference 2017.
Thomas Helmuth and Lee Spector. 2015. General Program Synthesis Benchmark Suite. In Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation (GECCO '15). ACM, New York, NY, USA, 1039--1046.
Thomas Helmuth, Lee Spector, Nicholas Freitag McPhee, and Saul Shanabrook. 2017. Linear Genomes for Structured Programs. In Genetic Programming Theory and Practice XIV, William P. Worzel, William Tozier, Brian W. Goldman, and Rick Riolo (Eds.). Springer.
John R. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press. http://mitpress.mit.edu/books/genetic-programming
William La Cava, Lee Spector, and Kourosh Danai. 2016. Epsilon-Lexicase Selection for Regression. In Proceedings of the Genetic and Evolutionary Computation Conference 2016 (GECCO '16). ACM, New York, NY, USA, 741--748.
Lee Spector. 2012. Assessment of Problem Modality by Differential Performance of Lexicase Selection in Genetic Programming: A Preliminary Report. In 1st workshop on Understanding Problems (GECCO-UP), Kent McClymont and Ed Keedwell (Eds.). ACM, Philadelphia, Pennsylvania, USA, 401--408. DOI:http://dx.doi.org/
Lee Spector and Thomas Helmuth. 2013. Uniform Linear Transformation with Repair and Alternation in Genetic Programming. In Genetic Programming Theory and Practice XI, Rick Riolo, Jason H. Moore, and Mark Kotanchek (Eds.). Springer, Ann Arbor, USA, Chapter 8, 137--153. DOI:http://dx.doi.org/
Lee Spector and Thomas Helmuth. 2014. Effective simplification of evolved push programs using a simple, stochastic hill-climber. In GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion, Christian Igel, Dirk V. Arnold, Christian Gagne, Elena Popovici, Anne Auger, Jaume Bacardit, Dimo Brockhoff, Stefano Cagnoni, Kalyanmoy Deb, Benjamin Doerr, James Foster, Tobias Glasmachers, Emma Hart, Malcolm I. Heywood, Hitoshi Iba, Christian Jacob, Thomas Jansen, Yaochu Jin, Marouane Kessentini, Joshua D. Knowles, William B. Langdon, Pedro Larranaga, Sean Luke, Gabriel Luque, John A. W. McCall, Marco A. Montes de Oca, Alison Motsinger-Reif, Yew Soon Ong, Michael Palmer, Konstantinos E. Parsopoulos, Guenther Raidl, Sebastian Risi, Guenther Ruhe, Tom Schaul, Thomas Schmickl, Bernhard Sendhoff, Kenneth O. Stanley, Thomas Stuetzle, Dirk Thierens, Julian Togelius, Carsten Witt, and Christine Zarges (Eds.). ACM, Vancouver, BC, Canada, 147--148. DOI:http://dx.doi.org/
Lee Spector, Jon Klein, and Maarten Keijzer. 2005. The Push3 execution stack and the evolution of control. In GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation. ACM Press, Washington DC, USA, 1689--1696.
Lee Spector and Alan Robinson. 2002. Genetic Programming and Autoconstructive Evolution with the Push Programming Language. Genetic Programming and Evolvable Machines 3, 1 (March 2002), 7--40.

Full Text:
... PantridgeMassMutual Financial GroupAmherst, Massachuse s, USAepantridge@massmutual.comLee SpectorHampshire CollegeAmherst, Massachuse s, USAlspector@hampshire.eduABSTRACT e PushGP genetic programming system, which evolves programsexpressed in the Push programming language, ... features for data science applications.CCS CONCEPTS?So ware and its engineering ? Genetic programming;? eory of computation? Evolutionary algorithms;KEYWORDSGenetic programming, Machine LearningACM Reference format:Edward ... Berlin, Germany, July 15-19, 2017,8 pages.DOI: h p://dx.doi.org/10.1145/3067695.30824681 INTRODUCTIONPushGP is a genetic programming system that has been under con-tinuous development, and has ...
... must be balanced.We use the name ?PushGP? to describe any genetic program thatevolves Push programs. Aside from using Push as the ... expressed, a PushGP system mightuse techniques employed in many other genetic programming sys-tems. For example, it might use various common techniques ... example, it might use various common techniques forparent selection and genetic variation, and many PushGP systemshave used techniques borrowed from tree-based ... selection and crossover based on the swapping ofsub-expressions.With respect to genetic variation, however, a linear representa-tion for Push programs has recently ... pyshgp is to be easily usablein more contexts than just genetic programming research. Ideally,this would foster an increase in awareness and ... increase in awareness and usage of PushGP notonly in the genetic programming community, but also in the widermachine learning community and ... to split into twomain parts: A Push languageinterpreter, and a genetic programming framework.3.1 e pyshgp Push Interpreter e Push language interpreter that ... exists as part of pyshgp is im-plemented independently of the genetic programming framework.It contains the implementation of a standard Push instruction ...
... in other contexts using just the Push inter-preter in pyshgp.3.2 Genetic Programming in pyshgpPushGP systems evolve programs in the Push language, ... Push language, which canbe executed using a Push interpreter. During genetic programmingruns, these programs must be evaluated by a user de ned ...
... 13].Push programs consist of nested lists of instructions and literals.Although genetic operators can be performed on Push programsdirectly, more recent PushGP ... use linear representations ofthese programs to allow for more exible genetic operators. eselinear representations are called Plush genomes [8]. e genetic pro-gramming that is implemented in pyshgp uses these Plush genomesduring ... a new value. e only recombination operator currently included in thepyshgp genetic programming framework is alternation [8, 14]. Al-ternation iterates over both ... the percent of each gen-eration that is created via each genetic operator, as well as combi-nations of genetic operators (ie. Number of children produced byAlternation followed by Uniform ... by Uniform Mutation).It is likely that these large, relatively complex genetic operatorsare not optimal for all problems that one would want ... future development and research is theimplementation of a many smaller genetic operators, and a morerobust way of chaining genetic operators together.3.3 Automatic Program Simpli cationPrograms in the Push language are ... present. A version of this problem arises inother forms of genetic programming as well. is issue has prompted most modern implementations ofPushGP, ...
... a standalone Python package. Itincluded the Push interpreter and a genetic programming frame-work but li le was included to help use the ... random code generation.genetic operator probabilities Probabilities of parents undergoing each genetic operator to produce a child.selection method Options are ?lexicase?[7], ?epsilon ...
... Most of the parameters needed to be set for a genetic programming run with pyshgp. Some parameters were notincluded to conserve ...
... one instruction beingtested is executed. e validation tests consist of small genetic programming runsusing the pyshgp genetic programming framework. ese smallruns are 5 generations of the example ... genomes, host language, and more. PushGPcontinues to be used in genetic programming research and improve-ments continue to be made. As the ... lower level language, such as C. e current state of the genetic programming framework inpyshgp could also be improved in a number ... a number of ways. Firstly, moregenetic operators that exist in genetic programming literature couldbe included in addition to the existing genetic operators. Second,the larger genetic
... together. is would allow for more experimen-tation and tuning of genetic operators. e pyshgp open source community is currently extremely small,and it ... contributions. A major goalof the pyshgp project is that the genetic programming and machinelearning communities engage in maintaining and improving thecodebase. ... of Evolved Programs through Automatic Simpli- cation. In Proceedings of the Genetic and Evolutionary Computation Conference2017.[5] omas Helmuth and Lee Spector. 2015. ... Synthesis BenchmarkSuite. In Proceedings of the 2015 Annual Conference on Genetic and EvolutionaryComputation (GECCO ?15). ACM, New York, NY, USA, 1039?1046. ... McPhee, and Saul Shanabrook.2017. Linear Genomes for Structured Programs. In Genetic Programming eoryand Practice XIV, William P. Worzel, William Tozier, Brian ... Brian W. Goldman, and RickRiolo (Eds.). Springer.[9] John R. Koza. Genetic Programming: On the Programming of Computersby Means of Natural Selection. ... Kourosh Danai. 2016. Epsilon-LexicaseSelection for Regression. In Proceedings of the Genetic and Evolutionary Compu-tation Conference 2016 (GECCO ?16). ACM, New York, ... of Problem Modality by Di erential Perfor-mance of Lexicase Selection in Genetic Programming: A Preliminary Report. In1st workshop on Understanding Problems (GECCO-UP), ... omas Helmuth. 2013. Uniform Linear Transformation withRepair and Alternation in Genetic Programming. In Genetic Programming e-ory and Practice XI, Rick Riolo, Jason H. Moore, ... GECCO Comp ?14: Proceed-ings of the 2014 conference companion on Genetic and evolutionary computationcompanion, Christian Igel, Dirk V. Arnold, Christian Gagne, ...
... of control. In GECCO 2005: Proceedings of the 2005 conferenceon Genetic and evolutionary computation. ACM Press, Washington DC, USA,1689?1696. DOI:h p://dx.doi.org/10.1145/1068009.1068292[17] Lee ... Washington DC, USA,1689?1696. DOI:h p://dx.doi.org/10.1145/1068009.1068292[17] Lee Spector and Alan Robinson. 2002. Genetic Programming and Autoconstruc-tive Evolution with the Push Programming Language. Genetic Programming andEvolvable Machines 3, 1 (March 2002), 7?40. DOI:h p://dx.doi.org/10.1023/A:10145385035431261GECCO ?17 ... PushGP Implementations3 PushGP in Python: pyshgp3.1 The pyshgp Push Interpreter3.2 Genetic Programming in pyshgp3.3 Automatic Program Simplification4 Usage4.1 Hyperparameters For Evolution4.2 ...

CCS:
Genetics

Keywords:
genetic circuits

Abstract:
<p>This special session paper reviews noise in genetic circuits, a relatively nascent field that has flourished ten years ...

Title:
Noise in genetic circuits: hindrance or chance?

References:
Ozbudak, E. M., Thattai, M., Kurtser, I., Grossman, A. D. and van Oudenaarden, A. Regulation of noise in the expression of a single gene. Nature genetics, 31, 1 (May 2002), 69--73.
Spencer, S. L., Gaudet, S., Albeck, J. G., Burke, J. M. and Sorger, P. K. Non-genetic origins of cell-to-cell variability in TRAIL-induced apoptosis. Nature, 459, 7245 (May 21 2009), 428--432.
Alon, U. Network motifs: theory and experimental approaches. Nature reviews. Genetics, 8, 6 (Jun 2007), 450--461.
Isaacs, F. J., Hasty, J., Cantor, C. R. and Collins, J. J. Prediction and measurement of an autoregulatory genetic module. Proceedings of the National Academy of Sciences of the United States of America, 100, 13 (Jun 24 2003), 7714--7719.

Full Text:
Noise in Genetic Circuits: Hindrance or Chance?Noise in Genetic Circuits: Hindrance or Chance? Cheng-Ju Pan Institute of Molecular and ... +886-2-33662481 hsiaochun@ntu.edu.tw ABSTRACT This special session paper reviews noise in genetic circuits, a relatively nascent field that has flourished ten years ... popularity of single-cell methods and synthetic biology approaches. Keywords Noise, Genetic circuits, Evolution 1. INTRODUCTION Genetic circuits composed of genes and regulatory elements connected in specific ... analyzed noise and its functions. Then we will discuss how genetic circuits modulate noise to deliver the overall systems behaviors. 2. ...
... This stochastic state provides B. subtilis chances to incorporate new genetic material that can potentially increase fitness of the population. The ... assays (over-expression, knockout or knockdown). De novo construction of synthetic genetic circuit with desired topologies (and work in isolation in a ...
... purposes; so can designers to optimize the performance of synthetic genetic circuits. Development of novel noise-modulating motifs is also an important ... Automated computations are thus very valuable aids for designing robust genetic circuits. Noise can be amplified in cascades [17], and theoretically ...
... of noise in the expression of a single gene. Nature genetics, , 31, 1 (May 2002), 69-73. [6] Li, G. W. ... Albeck, J. G., Burke, J. M. and Sorger, P. K. Non-genetic origins of cell-to-cell variability in TRAIL-induced apoptosis. Nature, 459, 7245 ... Alon, U. Network motifs: theory and experimental approaches. Nature reviews. Genetics, , 8, 6 (Jun 2007), 450-461. [13] Ruder, W. C., ... and Collins, J. J. Prediction and measurement of an autoregulatory genetic module. Proceedings of the National Academy of Sciences of the ...

CCS:
Genetics

Keywords:
genetic epidemiology
genetic programming
genetic analysis

Abstract:
<p>Genetic programming (GP) shows great promise for solving complex problems in ... (GP) shows great promise for solving complex problems in human genetics. . Unfortunately, many of these methods are not accessible to ... a comprehensive software package that puts powerful GP methods for genetic analysis in the hands of geneticists. It is our working ... seeks to facilitate geneticist-bioinformaticist-computer interactions for problem solving in human genetics.

Primary CCS:
Genetics

Sponsor:
ACM Special Interest Group on Genetic and Evolutionary Computation

Title:
Towards human-human-computer interaction for biologically-inspired problem-solving in human genetics

Full Text:
... Medical Center Dr. Lebanon, NH 03756 USA 603-653-9939 bill.c.white@dartmouth.edu ABSTRACT Genetic programming (GP) shows great promise for solving complex problems in ... (GP) shows great promise for solving complex problems in human genetics. . Unfortunately, many of these methods are not accessible to ... a comprehensive software package that puts powerful GP methods for genetic analysis in the hands of geneticists. It is our working ... seeks to facilitate geneticist-bioinformaticist-computer interactions for problem solving in human genetics. . More information can be found at www.epistasis.org or www.symbolicmodeler.org. ... J.3 [Computer Applications]: Life and Medical Sciences ? biology and genetics. . General Terms Algorithms, Design, Human Factors Keywords Genetic Analysis, Genetic Epidemiology, Genetic Programming, Open-Source Software, Symbolic Discriminant Analysis, Symbolic Regression. 1. INTRODUCTION ... Open-Source Software, Symbolic Discriminant Analysis, Symbolic Regression. 1. INTRODUCTION Human genetics is transitioning away from the study of single-gene Mendelian diseases ... methods that are able to model the relationship between multiple genetic and environmental factors and susceptibility to disease in the context ... goal of these endeavors is the identification and characterization of genetic risk factors that can be used to improve the detection, ... used to improve the detection, prevention and treatment of disease. Genetic algorithms, genetic programming, and other biologically-inspired computational intelligence methods show great promise ... for solving complex biomedical problems. This especially true in human genetics where these methods have been used to identify genetic risk factors for disease. Unfortunately, many of these methods are ... to develop a software package that would make available powerful genetic programming (GP) methods for data mining and machine learning to ... methods for data mining and machine learning to the human genetics community. There were several important objectives to the software design ...