iECGA: integer extended compact genetic algorithm

Extended compact genetic algorithm (ECGA) is an algorithm that can solve hard problems in the binary domain. ECGA is reliable and accurate because of the capability of detecting building blocks, but certain difficulties are encountered when we directly apply ECGA to problems in the integer domain. In this paper, we propose a new algorithm that extends ECGA, called integer extended compact genetic algorithm (iECGA). iECGA uses a modified probability model and inherits the capability of detecting building blocks from ECGA. iECGA is specifically designed for problems in the integer domain and can avoid the difficulties that ECGA encounters. With the experimental results, we show the performance comparisons between ECGA, iECGA, and a simple GA. The results indicate that iECGA has good performance on problems in the integer domain.

[1]  David E. Goldberg,et al.  A Survey of Optimization by Building and Using Probabilistic Models , 2002, Comput. Optim. Appl..

[2]  Martin Pelikan,et al.  Marginal Distributions in Evolutionary Algorithms , 2007 .

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

[4]  G. Harik.  Linkage Learning via Probabilistic Modeling in the ECGA , 1999 .

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

[6]  Ivo Everts,et al.  Extended Compact Genetic Algorithm , 2004 .

[7]  Ying-ping Chen,et al.  Extending the Scalability of Linkage Learning Genetic Algorithms: Theory and Practice , 2004 .

[8]  Shumeet Baluja,et al.  A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning , 1994 .

[9]  N. Yoshida,et al.  Multi-GAP: parallel and distributed genetic algorithms in VLSI , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

[10]  G. Harik.  Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms , 1997 .

[11]  H. Mühlenbein,et al.  From Recombination of Genes to the Estimation of Distributions I. Binary Parameters , 1996, PPSN.

[12]  D. Goldberg,et al.  BOA: the Bayesian optimization algorithm , 1999 .

[13]  Ja-Chen Lin,et al.  Image hiding by optimal LSB substitution and genetic algorithm , 2001, Pattern Recognit..

[14]  Luiz Antonio Nogueira Lorena,et al.  Constructive Genetic Algorithm for Clustering Problems , 2001, Evolutionary Computation.

[15]  David E. Goldberg,et al.  Designing Competent Mutation Operators Via Probabilistic Model Building of Neighborhoods , 2004, GECCO.

[16]  David E. Goldberg,et al.  Genetic Algorithm Design Inspired by Organizational Theory: Pilot Study of a Dependency Structure Matrix Driven Genetic Algorithm , 2003, GECCO.

[17]  Kalyanmoy Deb,et al.  Analyzing Deception in Trap Functions , 1992, FOGA.

[18]  David E. Goldberg,et al.  The Design of Innovation: Lessons from and for Competent Genetic Algorithms , 2002 .

[19]  Kalyanmoy Deb,et al.  Messy Genetic Algorithms Revisited: Studies in Mixed Size and Scale , 1990, Complex Syst..

[20]  D. Ackley.  A connectionist machine for genetic hillclimbing , 1987 .

[21]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[22]  David E. Goldberg,et al.  The compact genetic algorithm , 1999, IEEE Trans. Evol. Comput..

[23]  Jorma Rissanen,et al.  Stochastic Complexity in Statistical Inquiry , 1989, World Scientific Series in Computer Science.