Enhancing Model-building Efficiency in Extended Compact Genetic Algorithms

Masaharu Munetomo, Yuta Satake, Kiyoshi Akama

Abstract: Probabilistic model-building genetic algorithms such as extended compact genetic algorithm (ECGA) are proposed to solve difficult problems for classical genetic algorithms. Probabilistic model-building process based on marginal product model needs extensive computational overheads in ECGA. This paper discusses ECGA with linkage re-utilization and local search to reduce its model-building cost. Through simulation studies, we show the effectiveness of our approach that can reduce overall computational overheads.