Convergence Time for the Linkage Learning Genetic Algorithm

This paper identifies the sequential behavior of the linkage learning genetic algorithm, introduces the tightness time model for a single building block, and develops the connection between the sequential behavior and the tightness time model. By integrating the first-building-block model based on the sequential behavior, the tightness time model, and the connection between these two models, a convergence time model is constructed and empirically verified. The proposed convergence time model explains the exponentially growing time required by the linkage learning genetic algorithm when solving uniformly scaled problems.

[1]  Richard E. Neapolitan,et al.  Learning Bayesian networks , 2007, KDD '07.

[2]  Heinz Mühlenbein,et al.  FDA -A Scalable Evolutionary Algorithm for the Optimization of Additively Decomposed Functions , 1999, Evolutionary Computation.

[3]  D. E. Goldberg,et al.  Simple Genetic Algorithms and the Minimal, Deceptive Problem , 1987 .

[4]  P. Nordin,et al.  Explicitly defined introns and destructive crossover in genetic programming , 1996 .

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

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

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

[8]  James R. Levenick,et al.  Metabits: Generic Endogenous Crossover Control , 1995, International Conference on Genetic Algorithms.

[9]  Paul A. Viola,et al.  MIMIC: Finding Optima by Estimating Probability Densities , 1996, NIPS.

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

[11]  David E. Goldberg,et al.  Linkage Problem, Distribution Estimation, and Bayesian Networks , 2000, Evolutionary Computation.

[12]  Hitoshi Iba,et al.  Controlling Effective Introns for Multi-Agent Learning by Genetic Programming , 2000, GECCO.

[13]  David E. Goldberg,et al.  The Gambler's Ruin Problem, Genetic Algorithms, and the Sizing of Populations , 1999, Evolutionary Computation.

[14]  Astro Teller,et al.  A study in program response and the negative effects of introns in genetic programming , 1996 .

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

[16]  M. Pelikán,et al.  The Bivariate Marginal Distribution Algorithm , 1999 .

[17]  Shumeet Baluja,et al.  Using Optimal Dependency-Trees for Combinational Optimization , 1997, ICML.

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

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

[20]  J. A. Lozano,et al.  Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation , 2001 .

[21]  Jim Smith,et al.  Recombination strategy adaptation via evolution of gene linkage , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

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

[23]  D. Goldberg,et al.  Domino convergence, drift, and the temporal-salience structure of problems , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

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

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

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

[27]  David E. Goldberg,et al.  Tightness Time for the Linkage Learning Genetic Algorithm , 2003, GECCO.

[28]  Dirk Thierens,et al.  Advancing continuous IDEAs with mixture distributions and factorization selection metrics , 2001 .

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

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

[31]  Annie S. Wu,et al.  Studies on the effect of non-coding segments on the genetic algorithm , 1994, Proceedings Sixth International Conference on Tools with Artificial Intelligence. TAI 94.

[32]  L. A. Marascuilo,et al.  Nonparametric and Distribution-Free Methods for the Social Sciences , 1977 .

[33]  David E. Goldberg,et al.  Introducing Start Expression Genes to the Linkage Learning Genetic Algorithm , 2002, PPSN.

[34]  Masaharu Munetomo,et al.  Linkage identification based on epistasis measures to realize efficient genetic algorithms , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

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

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

[37]  James Smith,et al.  On Appropriate Adaptation Levels for the Learning of Gene Linkage , 2002, Genetic Programming and Evolvable Machines.

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

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

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

[41]  Dirk Thierens,et al.  Toward a Better Understanding of Mixing in Genetic Algorithms , 1993 .

[42]  S. Baluja,et al.  Using Optimal Dependency-Trees for Combinatorial Optimization: Learning the Structure of the Search Space , 1997 .

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

[44]  James R. Levenick.  Inserting Introns Improves Genetic Algorithm Success Rate: Taking a Cue from Biology , 1991, ICGA.

[45]  J. David Schaffer,et al.  An Adaptive Crossover Distribution Mechanism for Genetic Algorithms , 1987, ICGA.

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