论文信息 - Linkage identification based on epistasis measures to realize efficient genetic algorithms

Linkage identification based on epistasis measures to realize efficient genetic algorithms

Genetic algorithms (GAs) process building blocks (BBs) mixed and tested through genetic recombination operators. To realize effective BB processing, linkage identification, which detects a set of tightly linked loci, is essential. This paper proposes linkage identification with epistasis measures (LIEM), which detects linkage groups based on a pair-wise epistasis measure.

Masaharu Munetomo | M. Munetomo

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

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

[3] D.E. Goldberg,et al. A genetic algorithm using linkage identification by nonlinearity check , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

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

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

[6] Bart Naudts,et al. Epistasis as a Basic Concept in Formal Landscape Analysis , 1997, ICGA.

[7] Sanghamitra Bandyopadhyay,et al. Further Experimentations on the Scalability of the GEMGA , 1998, PPSN.

[8] Yuval Davidor,et al. Epistasis Variance: A Viewpoint on GA-Hardness , 1990, FOGA.

[9] H. Kargupta. SEARCH , Evolution , And The Gene Expression Messy Genetic Algorithm , 1994 .

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

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