creators_name: Whitacre, James M creators_name: Pham, Tuan Q creators_name: Sarker, Ruhul A type: confpaper datestamp: 2009-07-06 09:42:42 lastmod: 2011-03-11 08:57:23 metadata_visibility: show title: Credit Assignment in Adaptive Evolutionary Algorithms ispublished: pub subjects: comp-sci-art-intel full_text_status: public keywords: Evolutionary Algorithm, Genetic Algorithm, Adaptation, Historical Credit Assignment, Search Bias abstract: 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. date: 2006-07-08 date_type: published refereed: TRUE referencetext: [1] Barbosa, H. J. C. and e Sá, A. M. On Adaptive Operator Probabilities in Real Coded Genetic Algorithms, In Workshop on Advances and Trends in Artificial Intelligence for Problem Solving (SCCC '00), (Santiago, Chile, November 2000). [2] Davis, L. Handbook of Genetic Algorithms, van Nostrand Reinhold, New York, 1991. [3] De Jong, K. An analysis of the behaviour of a class of genetic adaptive systems. Ph. D Thesis, University of Michigan, Ann Arbor, Michigan, 1975. [4] Herrera, F. and Lozano, M. Tackling real-coded genetic algorithms: Operators and tools for the behavioural analysis, Artificial Intelligence Review 12, 4, (1998), 265-319. [5] Herrera, F., Lozano, M., and Sánchez, A. M. 2005. Hybrid crossover operators for real-coded genetic algorithms: an experimental study. Soft Comput. 9, 4 (Apr. 2005), 280-298. [6] Janka, E. Vergleich stochastischer Verfahren zur globalen Optimierung, Diploma Thesis, University of Vienna, Vienna, Austria, 1999. [7] Julstrom, B. A. Adaptive operator probabilities in a genetic algorithm that applies three operators. In Proceedings of the 1997 ACM Symposium on Applied Computing (SAC '97) (San Jose, California, United States). ACM Press, New York, NY, 233-238, 1997. [8] Muhlenbein, H., Schomisch, M. and Born, J. The parallel genetic algorithm as function optimizer. In Proc. of 4th International Conference of Genetic Algorithms, 271-278, 1991. [9] Pham, Q.T. Dynamic Optimization of Chemical Engineering Processes by an Evolutionary Method. Comp. Chem. Eng., 22 (1998), 1089-1097. [10] Pham, Q. T. Competitive evolution: a natural approach to operator selection. In: Progress in Evolutionary Computation, Lecture Notes in Artificial Intelligence, (Evolutionary Computation Workshop) (Armidale, Australia, November 21-22, 1994). Springer-Verlag, Heidelberg, 1995, 49-60. [11] Storn, R. and Price, K. Differential Evolution - A Simple and Efficient Adaptive Scheme for Global Optimization over Continuous Spaces. Technical Report TR-95-012, International Computer Science Institute, Berkeley, CA, 1995. [12] Whitacre, J., Pham, Q.T., Sarker, R. Use of Statistical Outlier Detection Method in Adaptive Evolutionary Algorithms. In Proceedings of the 2006 Conference on Genetic and Evolutionary Computation (GECCO '05) (Seattle, USA, July 8-12, 2006). ACM Press, New York, NY, 2006. citation: Whitacre, Dr James M and Pham, Dr Tuan Q and Sarker, Dr Ruhul A (2006) Credit Assignment in Adaptive Evolutionary Algorithms. [Conference Paper] document_url: http://cogprints.org/6580/1/Credit_Assignment_in_adaptive_evolutionary_algorithms-whitacre.pdf