Operator Self-adaptation in Genetic Programming

We investigate the application of adaptive operator selection rates to Genetic Programming. Results confirm those from other areas of evolutionary algorithms: adaptive rate selection out-performs non-adaptive methods, and among adaptive methods, adaptive pursuit out-performs probability matching. Adaptive pursuit combined with a reward policy that rewards the overall fitness change in the elite worked best of the strategies tested, though not uniformly on all problems.

[1]  Nguyen Xuan Hoai,et al.  Representation and structural difficulty in genetic programming , 2006, IEEE Transactions on Evolutionary Computation.

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

[3]  Nguyen Xuan Hoai,et al.  Some Experimental Results with Tree Adjunct Grammar Guided Genetic Programming , 2002, EuroGP.

[4]  David E. Goldberg,et al.  Probability Matching, the Magnitude of Reinforcement, and Classifier System Bidding , 1990, Machine Learning.

[5]  Aravind K. Joshi,et al.  Tree Adjunct Grammars , 1975, J. Comput. Syst. Sci..

[6]  Anthony Brabazon,et al.  Tree-adjunct grammatical evolution , 2010, IEEE Congress on Evolutionary Computation.

[7]  Kenneth de Jong.  Parameter Setting in EAs: a 30 Year Perspective , 2007 .

[8]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[9]  Simon M. Lucas,et al.  Parallel Problem Solving from Nature - PPSN X, 10th International Conference Dortmund, Germany, September 13-17, 2008, Proceedings , 2008, PPSN.

[10]  Hans-Paul Schwefel,et al.  Numerical Optimization of Computer Models , 1982 .

[11]  Michèle Sebag,et al.  Dynamic Multi-Armed Bandits and Extreme Value-Based Rewards for Adaptive Operator Selection in Evolutionary Algorithms , 2009, LION.

[12]  Dirk Thierens,et al.  Adaptive Strategies for Operator Allocation , 2007, Parameter Setting in Evolutionary Algorithms.

[13]  Michèle Sebag,et al.  Extreme Value Based Adaptive Operator Selection , 2008, PPSN.

[14]  Zbigniew Michalewicz,et al.  Parameter Setting in Evolutionary Algorithms , 2007, Studies in Computational Intelligence.

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

[16]  M. A. L. THATHACHAR,et al.  A new approach to the design of reinforcement schemes for learning automata , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[17]  Christian Igel,et al.  Operator adaptation in evolutionary computation and its application to structure optimization of neural networks , 2003, Neurocomputing.

[18]  P. S. Sastry,et al.  A Class of Rapidly Converging Algorithms for Learning Automata , 1984 .