Tightness Time for the Linkage Learning Genetic Algorithm

This paper develops a model for tightness time, linkage learning time for a single building block, in the linkage learning genetic algorithm (LLGA). First, the existing models for both linkage learning mechanisms, linkage skew and linkage shift, are extended and investigated. Then, the tightness time model is derived and proposed based on the extended linkage learning mechanism models. Experimental results are also presented in this study to verify the extended models for linkage learning mechanisms and the proposed model for tightness time.

[1]  James R. Levenick,et al.  Swappers: introns promote flexibility, diversity and invention , 1999 .

[2]  Hillol Kargupta,et al.  The Gene Expression Messy Genetic Algorithm , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[3]  W. Beyer CRC Standard Probability And Statistics Tables and Formulae , 1990 .

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

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

[6]  D. Goldberg,et al.  Linkage learning through probabilistic expression , 2000 .

[7]  F. Oppacher,et al.  The benefits of computing with introns , 1996 .

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

[9]  David E. Goldberg,et al.  Linkage Identification by Non-monotonicity Detection for Overlapping Functions , 1999, Evolutionary Computation.

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

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

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

[13]  Rafal Salustowicz,et al.  Probabilistic Incremental Program Evolution , 1997, Evolutionary Computation.

[14]  Heinz Mühlenbein,et al.  The Equation for Response to Selection and Its Use for Prediction , 1997, Evolutionary Computation.

[15]  Kalyanmoy Deb,et al.  RapidAccurate Optimization of Difficult Problems Using Fast Messy Genetic Algorithms , 1993, ICGA.

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

[17]  Annie S. Wu,et al.  Testing the Robustness of the Genetic Algorithm on the Floating Building Block Representation , 1996, AAAI/IAAI, Vol. 1.

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

[19]  David E. Goldberg,et al.  Learning Linkage , 1996, FOGA.

[20]  Dirk Thierens,et al.  Linkage Information Processing In Distribution Estimation Algorithms , 1999, GECCO.

[21]  Masaharu Munetomo,et al.  Identifying Linkage Groups by Nonlinearity/Non-monotonicity Detection , 1999 .

[22]  Dirk Thierens,et al.  Mixing in Genetic Algorithms , 1993, ICGA.