Empirical investigations on parallel competent genetic algorithms

This paper empirically investigates parallel competent genetic algorithms (cGAs) [4]. cGAs, such as BOA [21], LINCGA [15], D5 -GA [28], can solve GA-difficult problems by automatically learning problem structure as gene linkage. Parallel implementation of cGAs can reduce computational cost due to the linkage learning and give us problem solving environments for a wide spectrum of real-world problems. Although some parallel cGAs have been proposed [16,18,19], the effect of the parallelizations has not been investigated enough. This paper empirically discusses the applicability and property of parallel cGAs, including a new parallel cGA, parallel D5 -GA. Categories and Subject Descriptors I.2.8 [Artificial Intelligence]: Problem Solving, Control Methods, and Search General Terms Algorithms Keywords Linkage, Parallelization

• Type: Conference
• Year: 2008
• Where: GECCO
• Authors: Miwako Tsuji, Masaharu Munetomo, Kiyoshi Akama
• Keywords: GECCO 2008, Optimization, Parallel Cga, Parallel Implementation, Problem Solving