Credit assignment in adaptive evolutionary algorithms

In this paper, a new method for assigning credit to search operators is presented. Starting with the principle of optimizing search bias, search operators are selected based on an ability to create solutions that are historically linked to future generations. Using a novel framework for defining performance measurements, distributing credit for performance, and the statistical interpretation of this credit, a new adaptive method is developed and shown to outperform a variety of adaptive and non-adaptive competitors.

[1]  R. Storn,et al.  Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces , 2004 .

[2]  Ruhul A. Sarker,et al.  Use of statistical outlier detection method in adaptive evolutionary algorithms , 2006, GECCO.

[3]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

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

[5]  Francisco Herrera,et al.  Hybrid crossover operators for real-coded genetic algorithms: an experimental study , 2005, Soft Comput..

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

[7]  H. Barbosa.  On Adaptive Operator Probabilities in Real Coded Genetic Algorithms , 2000 .

[8]  Q. Pham.  Dynamic optimization of chemical engineering processes by an evolutionary method , 1998 .

[9]  Q. Tuan Pham,et al.  Competitive Evolution: A Natural Approach to Operator Selection , 1993, Evo Workshops.

[10]  Heinz Mühlenbein,et al.  The parallel genetic algorithm as function optimizer , 1991, Parallel Comput..

[11]  Francisco Herrera,et al.  Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis , 1998, Artificial Intelligence Review.

[12]  Bryant A. Julstrom,et al.  Adaptive operator probabilities in a genetic algorithm that applies three operators , 1997, SAC '97.

[13]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..