Population Sizing of Dependency Detection by Fitness Difference Classification

Recently, the linkage problem has attracted attention from researchers and users of genetic algorithms and many efforts have been undertaken to learn linkage. Especially, (1) perturbation methods (PMs) and (2) estimation of distribution algorithms (EDAs) are well known and frequently employed for linkage identification. In our previous work [TMA04], we have proposed a novel approach called Dependency Detection for Distribution Derived from df (D5) which inherits characteristics from both EDAs and PMs. It detects dependencies of loci by estimating the distributions of strings classified according to fitness differences and can solve EDA difficult problems requiring a smaller number of fitness evaluations. In this paper, we estimate population size for the D5 and its computation cost. The computation cost slightly exceeds O(l), which is less than the PMs and some of EDAs.

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

[2]  Alden H. Wright,et al.  Efficient Linkage Discovery by Limited Probing , 2003, GECCO.

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

[4]  David E. Goldberg,et al.  Time Complexity of genetic algorithms on exponentially scaled problems , 2000, GECCO.

[5]  Matthew J. Streeter.  Upper Bounds on the Time and Space Complexity of Optimizing Additively Separable Functions , 2004, GECCO.

[6]  David E. Goldberg,et al.  Scalability of the Bayesian optimization algorithm , 2002, Int. J. Approx. Reason..

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

[8]  H. Kargupta.  Search, polynomial complexity, and the fast messy genetic algorithm , 1996 .

[9]  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).

[10]  E. Cantu-Paz,et al.  The Gambler's Ruin Problem, Genetic Algorithms, and the Sizing of Populations , 1997, Evolutionary Computation.

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

[12]  Masaharu Munetomo,et al.  Modeling Dependencies of Loci with String Classification According to Fitness Differences , 2004, GECCO.

[13]  Kalyanmoy Deb,et al.  A Comparative Analysis of Selection Schemes Used in Genetic Algorithms , 1990, FOGA.