Analysis of evolutionary multi-tasking as an island model

ABSTRACT

Recently, an idea of evolutionary multi-tasking has been proposed and applied to various types of optimization problems. The basic idea of evolutionary multi-tasking is to simultaneously solve multiple optimization problems (i.e., tasks) in a cooperative manner by a single run of an evolutionary algorithm. For this purpose, each individual in a population has its own task. This means that a population of individuals can be viewed as being divided into multiple sub-populations. The number of sub-populations is the same as the number of tasks to be solved. In this paper, first we explain that a multi-factorial evolutionary algorithm (MFEA), which is a representative algorithm of evolutionary multi-tasking, can be viewed as a special island model. MFEA has the following two features: (i) Crossover is performed not only within an island but also between islands, and (ii) no migration is performed between islands. Information of individuals in one island is transferred to another island through inter-island crossover. Next, we propose a simple implementation of evolutionary multi-tasking in the framework of the standard island model. Then, we compare our island model with MFEA through computational experiments. Promising results are obtained by our implementation of evolutionary multi-tasking.

References

1. E. Cantú-Paz. 1998. A survey of parallel genetic algorithms, Calculateurs Paralleles 10, 2 (1998), 141--171.
2. B. Da, Y. S. Ong, L. Feng, A.K. Qin, A. Gupta, Z. Zhu, C. K. Ting, K. Tang, X. Yao. 2017. Evolutionary multitasking for single-objective continuous optimization: benchmark problems, performance metric, and baseline results. arXiv preprint arXiv:1706.03470 (2017)
3. J. Ding, C. Yang, Y. Jin, T. Chai. 2017. Generalized multi-tasking for evolutionary optimization of expensive problems, IEEE Trans. on Evolutionary Computation (Early Access Paper: Online Available from IEEE Xplore)
4. M. Gorges-Schleuter. 1990. Explicit parallelism of genetic algorithms through population structures. In Proceedings of International Conference of Parallel Problem Solving from Nature I (PPSN 1990), Springer, Dortmund, Germany, 150--159.
5. A. Gupta, Y. S. Ong, and L. Feng. 2016. Multifactorial evolution: Towards evolutionary multitasking. IEEE Trans. on Evolutionary Computation 20, 3(2016), 343--357.
6. A. Gupta, Y. S. Ong, L. Feng and K. C. Tan. 2017. Multiobjective multifactorial optimization in evolutionary multitasking. IEEE Trans. on Cybernetics 47, 7(2017), 1652--1665.
7. A. Gupta, B. Da, Y. Yuan and Y. S. Ong. 2017. On the emerging notion of evolutionary multitasking: A Computational analog of cognitive multitasking. Recent Advances in Evolutionary Multi-objective Optimization. New York, USA: Springer, 2017, 139--157.
8. A. Gupta, Y. S. Ong, L. Feng. 2015. Evolutionary multitasking in bi-level optimization. Complex & Intelligent Systems 1, 1--4 (2015), 83--95.
9. Y. S. Ong, A. Gupta. 2016. Evolutionary multitasking: A computer science view of cognitive multitasking. Cognitive Computation 8, 2 (2016), 521--544.
10. L. Pan, C. He, C. He, Y. Tian, H. Wang, X. Zhang, and Y. Jin. 2018. A Classification Based Surrogate-Assisted Evolutionary Algorithm for Expensive Many-Objective Optimization. IEEE Trans. on Evolutionary Computation (Early Access Paper: Online Available from IEEE Xplore)
11. Y. W, Wen and C. K. Ting. 2017. Parting ways and reallocating resources in evolutionary multitasking. In Proceeding of 2017 Congress on Evolutionary Computation (CEC 2017), IEEE, San Sebastián, Spain, 1295--1302.