Use of statistical outlier detection method in adaptive evolutionary algorithms

In this paper, the issue of adapting probabilities for Evolutionary Algorithm (EA) search operators is revisited. A framework is devised for distinguishing between measurements of performance and the interpretation of those measurements for purposes of adaptation. Several examples of measurements and statistical interpretations are provided. Probability value adaptation is tested using an EA with 10 search operators against 10 test problems with results indicating that both the type of measurement and its statistical interpretation play significant roles in EA performance. We also find that selecting operators based on the prevalence of outliers rather than on average performance is able to provide considerable improvements to adaptive methods and soundly outperforms the non-adaptive case.

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

[2]  Dirk Thierens,et al.  An Adaptive Pursuit Strategy for Allocating Operator Probabilities , 2005, BNAIC.

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

[4]  Ek Peng Chew,et al.  Variable period adaptive genetic algorithm , 2002 .

[5]  Peter Ross,et al.  Cost Based Operator Rate Adaption: An Investigation , 1996, PPSN.

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

[7]  Tzung-Pei Hong,et al.  Evolution of Appropriate Crossover and Mutation Operators in a Genetic Process , 2001, Applied Intelligence.

[8]  Zbigniew Michalewicz,et al.  Parameter Control in Evolutionary Algorithms , 2007, Parameter Setting in Evolutionary Algorithms.

[9]  N. Packard,et al.  Evolution of evolvability via adaptation of mutation rates. , 2003, Bio Systems.

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

[11]  Jim Smith,et al.  Operator and parameter adaptation in genetic algorithms , 1997, Soft Comput..

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

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

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

[15]  D. Boeringer,et al.  A simultaneous parameter adaptation scheme for genetic algorithms with application to phased array synthesis , 2005, IEEE Transactions on Antennas and Propagation.

[16]  Ruhul A. Sarker,et al.  Credit assignment in adaptive evolutionary algorithms , 2006, GECCO '06.

[17]  Kwong-Sak Leung,et al.  A novel approach in parameter adaptation and diversity maintenance for genetic algorithms , 2003, Soft Comput..

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

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

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

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

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

[23]  József Szabados,et al.  Trends and Applications in Constructive Approximation , 2006 .

[24]  David E. Goldberg,et al.  Adaptive Hybrid Genetic Algorithm for Groundwater Remediation Design , 2005 .

[25]  Christian Igel,et al.  Evolutionary Optimization of Neural Systems: The Use of Strategy Adaptation , 2005 .

[26]  Francisco Herrera,et al.  Adaptive genetic operators based on coevolution with fuzzy behaviors , 2001, IEEE Trans. Evol. Comput..