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A genetic algorithms tutorial tool for numerical function optimisation

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Published:04 June 1997Publication History
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Abstract

The field of Genetic Algorithms has grown into a huge area over the last few years. Genetic Algorithms are adaptive methods, which can be used to solve search and optimisation problems over a period of generations, based upon the principles of natural selection and survival of the fittest. This paper describes an innovative tool to introduce the basics of the subject of Genetic Algorithms called GAVIn (Genetic Algorithms Visual Interface). It focuses on the domain of numerical function optimisation problems as these form a good basis for learning and operator comparison. The other problem domains are too varied and problem dependent to form a general, robust learning tool.

References

  1. 1 Schaffer JD, Caruana RA, Eshelman LJ and Das R, A Study of Control Parameters Affecting Online Performance of Genetic Algorithms for Function Optimisation. Proceedings of the Third international Conference on Genetic Algorithms, Morgan-Kaufmann Publishers, 1989.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. 2 Burke EK, Elliman DG and Weare RF, A Genetic Algorithm based University Timetabling System. 2~ East-West International Conference on Computer Technologies in Education, vol. 1, pp. 35-40, 1994. Department of Computer Science, University of Nottingham, UK.]]Google ScholarGoogle Scholar
  3. 3 Colomi A, Dorigo M and Maniezzo V, Genetic Algorithms: A New Approach to the Timetabling Approach. NATO-ASI Series, vol. F82 - Combinatorial Optimisation, pp. 235-239, Springer-Verlag, Berlin Heidelberg, 1992. Dipartimento di Electronica, Policecnico di Milano, Italy.]]Google ScholarGoogle Scholar
  4. 4 Burke EK, Newall JP and Weare RF, A Memetic Algorithm for University Exam Timetabling. The Practice and Theory of Automated Timetabling, ed. EK Burke and P Ross, Springer-Verlag (Lecture Notes in Computer Science), 1996. Department of Computer Science, University of Nottingham, UK.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. 5 Kosak C, Marks J and Schieber S, A Parallel Genetic Algorithm for Network Design Layout. Proceedings of the Fourth International Conference on Genetic Algorithms, pp. 458-465, Morgan-Kaufmann Publishers, 1991. Division of Applied Sciences, Harvard University, USA.]]Google ScholarGoogle Scholar
  6. 6 Riolo R, Modelling Simple Human Catergory Learning with a Classifier System. Proceedings of the Fourth International Conference on Genetic Algorithms, pp. 324-333, Morgan- Kaufmann Publishers, 1991. University of Michigan, USA.]]Google ScholarGoogle Scholar
  7. 7 Bellengue S, G53AAI: Advanced Artificial Intelligence Course, Department of Computer Science, University of Nottingham, UK.]]Google ScholarGoogle Scholar
  8. 8 DeJong K, An Analysis of the Behaviour of a Class of Genetic Adaptive Systems. PhD Thesis. Department of Computer and Communication Sciences, University of Michigan, USA.]]Google ScholarGoogle Scholar
  9. 9 Muhlenbein H and Schlierkamp-Voosen D, Predictive Models for Breeder Genetic Algorithms. Journal of Evolutionary Computation, 1993.]]Google ScholarGoogle Scholar
  10. 10 Craighurst R and Martin W, Enhancing GA Performance through Crossover Prohibitions Based on Ancestry. Proceedings of the Sixth International Conference on Genetic Algorithms, Morgan-Kaufmann Publishers, 1995. Department of Computer Science, University of Virginia, USA.]] Google ScholarGoogle ScholarDigital LibraryDigital Library

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                  • Published in

                    cover image ACM SIGCSE Bulletin
                    ACM SIGCSE Bulletin  Volume 29, Issue 3
                    Sept. 1997
                    143 pages
                    ISSN:0097-8418
                    DOI:10.1145/268809
                    Issue’s Table of Contents
                    • cover image ACM Conferences
                      ITiCSE '97: Proceedings of the 2nd conference on Integrating technology into computer science education
                      June 1997
                      147 pages
                      ISBN:0897919238
                      DOI:10.1145/268819

                    Copyright © 1997 ACM

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                    Association for Computing Machinery

                    New York, NY, United States

                    Publication History

                    • Published: 4 June 1997

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