Population sizing of dependency detection by fitness difference classification

Authors:
Miwako Tsuji;Masaharu Munetomo;Kiyoshi Akama
Affiliations:
Hokkaido University, Sapporo, Japan;Hokkaido University, Sapporo, Japan;Hokkaido University, Sapporo, Japan
Venue:
FOGA'05 Proceedings of the 8th international conference on Foundations of Genetic Algorithms
Year:
2005

Citing 1
Cited 1

Efficient linkage discovery by limited probing

GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI

Linkage identification by fitness difference clustering

Evolutionary Computation

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Abstract

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.