Time dependent optimization with a folding genetic algorithm

Time-dependent optimization has revealed to be a promising gap for the entire genetic algorithms community since it has numerous applications. This paper extends previous work (Collard et al., 1996) related to the use of meta-genes in the so-called dual genetic algorithms (DGAs). A more generic framework, involving a variable number of genes, is introduced. Folding genetic algorithms are thus proposed as a new class of genetic algorithms, whose effectiveness is investigated on two well-known models of dynamical environments and compared to simple genetic algorithms and DGAs. Eventually, further analysis of these results enlightens the ability of FGAs to evolve a metric over the search space (i.e. a kind of encoding scheme) along with potential solutions. These particularly encouraging results open up interesting perspectives, as FGAs could be applied to to other fundamental problems investigated by the genetic algorithms community in order to measure the benefits of this really meta-level of evolution.

[1]  Philippe Collard,et al.  DGP: How To Improve Genetic Programming with Duals , 1997, ICANNGA.

[2]  Philippe Collard,et al.  An evolutionary approach for time dependant optimization , 1996, Proceedings Eighth IEEE International Conference on Tools with Artificial Intelligence.

[3]  John J. Grefenstette,et al.  Genetic Algorithms for Tracking Changing Environments , 1993, ICGA.

[4]  James R. Levenick,et al.  Metabits: Generic Endogenous Crossover Control , 1995, International Conference on Genetic Algorithms.

[5]  Philippe Collard,et al.  DGA: An Efficient Genetic Algorithm , 1994, ECAI.

[6]  Paul Helman,et al.  An immunological approach to change detection: algorithms, analysis and implications , 1996, Proceedings 1996 IEEE Symposium on Security and Privacy.

[7]  Kok Cheong Wong,et al.  A New Diploid Scheme and Dominance Change Mechanism for Non-Stationary Function Optimization , 1995, ICGA.

[8]  John J. Grefenstette,et al.  Genetic Algorithms for Changing Environments , 1992, PPSN.

[9]  Philippe Collard,et al.  Fitness Distance Correlation in a Dual Genetic Algorithm , 1996, ECAI.

[10]  James R. Levenick.  Inserting Introns Improves Genetic Algorithm Success Rate: Taking a Cue from Biology , 1991, ICGA.

[11]  Philippe Collard,et al.  Relational Schemata: A Way to Improve the Expressiveness of Classifiers , 1995, ICGA.

[12]  Philippe Collard,et al.  'Royal-Road' Landscapes for a Dual Genetic Algorithm , 1996, ECAI.

[13]  Terence C. Fogarty,et al.  A Comparative Study of Steady State and Generational Genetic Algorithms , 1996, Evolutionary Computing, AISB Workshop.

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