Adaptive discretization for probabilistic model building genetic algorithms

This paper proposes an adaptive discretization method, called Split-on-Demand (SoD), to enable the probabilistic model building genetic algorithm (PMBGA) to solve optimization problems in the continuous domain. The procedure, effect, and usage of SoD are described in detail. As an example, the integration of SoD and the extended compact genetic algorithm (ECGA), named real-coded ECGA (rECGA), is presented and numerically examined. The experimental results indicate that rECGA works well and SoD is effective. The behavior of SoD is analyzed and discussed, followed by the potential future work for SoD. Categories and Subject Descriptors I.2.6 [Artificial Intelligence]: Learning--Parameter learning; I.2.8 [Artificial Intelligence]: Problem Solving, Control Methods, and Search--Heuristic methods General Terms Algorithms Keywords Adaptive discretization, split-on-demand, extended compact genetic algorithm, real-parameter optimization

• Type: Conference
• Year: 2006
• Where: GECCO
• Authors: Chao-Hong Chen, Wei-Nan Liu, Ying-Ping Chen
• Keywords: Adaptive Discretization, Compact Genetic Algorithm, GECCO 2006, Genetic Algorithm, Optimization