Linkage learning, overlapping building blocks, and systematic strategy for scalable recombination

This paper aims at an important, but poorly studied area in genetic algorithm (GA) field: How to design the crossover operator for problems with overlapping building blocks (BBs). To investigate this issue systematically, the relationship between an inaccurate linkage model and the convergence time of GA is studied. Specifically, the effect of the error of so-called false linkage is analogized to a lower exchange probability of uniform crossover. The derived qualitative convergence-time model is used to develop a scalable recombination strategy for problems with overlapping BBs. A set of problems with circularly overlapping BBs exemplify the recombination strategy.

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

[2]  Dirk Thierens,et al.  Convergence Models of Genetic Algorithm Selection Schemes , 1994, PPSN.

[3]  D. Goldberg,et al.  How Well Does A Single-Point Crossover Mix Building Blocks with Tight Linkage? , 2004 .

[4]  Magnus Rattray,et al.  Noisy Fitness Evaluation in Genetic Algorithms and the Dynamics of Learning , 1996, FOGA.

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

[6]  Yuval Rabani,et al.  A computational view of population genetics , 1995, STOC '95.

[7]  David E. Goldberg,et al.  Toward an Understanding of the Quality and Efficiency of Model Building for Genetic Algorithms , 2004, GECCO.

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

[9]  Heinz Mühlenbein,et al.  Predictive Models for the Breeder Genetic Algorithm I. Continuous Parameter Optimization , 1993, Evolutionary Computation.

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

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

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

[13]  A. Sinclair,et al.  A computational view of population genetics , 1998 .

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

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

[16]  Petr Posík.  Estimation of Distribution Algorithms , 2006 .

[17]  Zbigniew Michalewicz,et al.  Evolutionary Computation 1 , 2018 .

[18]  Kalyanmoy Deb,et al.  Genetic Algorithms, Noise, and the Sizing of Populations , 1992, Complex Syst..

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