A coevolutionary chromosome encoding scheme for high dimensional search spaces

This work introduces a co evolutionary chromosome encoding scheme for evolving solutions in a high dimensional search space. The chromosome is divided in m “genes” and m different populations are created (one population per gene). Each one of the m populations evolves an specific gene and good references to genes in the remaining populations. The candidate solution is built using such references and the encoded gene. Individuals in the same population compete among them to find the best gene while individuals from different populations work together in order to find the best candidate solution. Finally, the best candidate solution is selected from all the populations based on its performance. Some experiments are conducted on well-known binary and real defined functions using three different evolutionary techniques. The obtained results indicate that the proposed approach is able to improve the underline evolutionary technique when evolving solutions for optimization problems in high dimensional spaces.

[1]  Jonatan Gómez,et al.  Self Adaptation of Operator Rates in Evolutionary Algorithms , 2004, GECCO.

[2]  Peter Ross,et al.  Adapting Operator Settings in Genetic Algorithms , 1998, Evolutionary Computation.

[3]  Jonatan Gómez,et al.  COFRE: a fuzzy rule coevolutionary approach for multiclass classification problems , 2005, 2005 IEEE Congress on Evolutionary Computation.

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

[5]  Jan Paredis,et al.  Coevolutionary Computation , 1995, Artificial Life.

[6]  D. Sofge,et al.  A blended population approach to cooperative coevolution for decomposition of complex problems , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[7]  Kalyanmoy Deb,et al.  Messy Genetic Algorithms: Motivation, Analysis, and First Results , 1989, Complex Syst..

[8]  Xin Yao,et al.  Large scale evolutionary optimization using cooperative coevolution , 2008, Inf. Sci..

[9]  Anabela Simões,et al.  Transposition: A Biological-Inspired Mechanism to Use with Genetic Algorithms , 1999, ICANNGA.

[10]  Kenneth A. De Jong,et al.  A Cooperative Coevolutionary Approach to Function Optimization , 1994, PPSN.

[11]  Melanie Mitchell,et al.  Relative Building-Block Fitness and the Building Block Hypothesis , 1992, FOGA.

[12]  Richard K. Belew,et al.  New Methods for Competitive Coevolution , 1997, Evolutionary Computation.

[13]  Thomas Bäck,et al.  Evolutionary algorithms in theory and practice - evolution strategies, evolutionary programming, genetic algorithms , 1996 .