Spatially-Structured Sharing Technique for Multimodal Problems

Spatially-structured populations are one approach to increasing genetic diversity in an evolutionary algorithm (EA). However, they are susceptible to convergence to a single peak in a multimodal fitness landscape. Niching methods, such as fitness sharing, allow an EA to maintain multiple solutions in a single population, however they have rarely been used in conjunction with spatially-structured populations. This paper introduces local sharing, a method that applies sharing to the overlapping demes of a spatially-structured population. The combination of these two methods succeeds in maintaining multiple solutions in problems that have previously proved difficult for sharing alone (and vice-versa).

[1]  Kalyanmoy Deb,et al.  Massive Multimodality, Deception, and Genetic Algorithms , 1992, PPSN.

[2]  Samir W. Mahfoud.  Population Size and Genetic Drift in Fitness Sharing , 1994, FOGA.

[3]  Kalyanmoy Deb,et al.  An Investigation of Niche and Species Formation in Genetic Function Optimization , 1989, ICGA.

[4]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[5]  Alan S. Perelson,et al.  Searching for Diverse, Cooperative Populations with Genetic Algorithms , 1993, Evolutionary Computation.

[6]  Y. J. Cao,et al.  Evolutionary programming , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[7]  P. John Clarkson,et al.  A Species Conserving Genetic Algorithm for Multimodal Function Optimization , 2002, Evolutionary Computation.

[8]  P. John Clarkson,et al.  Erratum: A Species Conserving Genetic Algorithm for Multimodal Function Optimization , 2003, Evolutionary Computation.

[9]  Claudio De Stefano,et al.  On the role of population size and niche radius in fitness sharing , 2004, IEEE Transactions on Evolutionary Computation.

[10]  Xin Yao,et al.  Every Niching Method has its Niche: Fitness Sharing and Implicit Sharing Compared , 1996, PPSN.

[11]  Alan S. Perelson,et al.  Using Genetic Algorithms to Explore Pattern Recognition in the Immune System , 1993, Evolutionary Computation.

[12]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[13]  Jayshree Sarma,et al.  An analysis of decentralized and spatially distributed genetic algorithms , 1998 .

[14]  James E. Baker,et al.  Reducing Bias and Inefficienry in the Selection Algorithm , 1987, ICGA.

[15]  Grant Dick.  A Comparison of Localised and Global Niching Methods , 2005 .

[16]  Alain Pétrowski,et al.  A clearing procedure as a niching method for genetic algorithms , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[17]  Xiaodong Li,et al.  Adaptively choosing niching parameters in a PSO , 2006, GECCO.

[18]  J. Watson.  A Performance Assessment of Modern Niching Methods for Parameter Optimization Problems , 1999 .

[19]  Hans-Paul Schwefel,et al.  Parallel Problem Solving from Nature — PPSN IV , 1996, Lecture Notes in Computer Science.

[20]  Samir W. Mahfoud.  A Comparison of Parallel and Sequential Niching Methods , 1995, ICGA.

[21]  David E. Goldberg,et al.  A Note on Boltzmann Tournament Selection for Genetic Algorithms and Population-Oriented Simulated Annealing , 1990, Complex Syst..

[22]  Xiaodong Li,et al.  This article has been accepted for inclusion in a future issue. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 1 Locating and Tracking Multiple Dynamic Optima by a Particle Swarm Model Using Speciation , 2022 .

[23]  Ralph R. Martin,et al.  A Sequential Niche Technique for Multimodal Function Optimization , 1993, Evolutionary Computation.

[24]  Kenneth Alan De Jong,et al.  An analysis of the behavior of a class of genetic adaptive systems. , 1975 .

[25]  Michael J. Shaw,et al.  Genetic algorithms with dynamic niche sharing for multimodal function optimization , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[26]  S. Wright,et al.  Isolation by Distance. , 1943, Genetics.

[27]  David E. Goldberg,et al.  Genetic Algorithms with Sharing for Multimodalfunction Optimization , 1987, ICGA.

[28]  Bruno Sareni,et al.  Fitness sharing and niching methods revisited , 1998, IEEE Trans. Evol. Comput..

[29]  Sam Kwong,et al.  Genetic algorithms and their applications , 1996, IEEE Signal Process. Mag..

[30]  Samir W. Mahfoud.  Niching methods for genetic algorithms , 1996 .

[31]  Xiaodong Li,et al.  Enhancing the robustness of a speciation-based PSO , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[32]  David E. Goldberg,et al.  Genetic Algorithm Difficulty and the Modality of Fitness Landscapes , 1994, FOGA.

[33]  K. Dejong,et al.  An analysis of the behavior of a class of genetic adaptive systems , 1975 .

[34]  William M. Spears,et al.  Simple Subpopulation Schemes , 1998 .

[35]  Jeffrey Horn,et al.  The nature of niching: genetic algorithms and the evolution of optimal, cooperative populations , 1997 .

[36]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[37]  E. Mayr.  Populations, Species, and Evolution, An Abridgment of Animal Species and Evolution , 1970 .