Fitness-based neighbor selection for multimodal function optimization

We propose a selection scheme called Fitness-based Neighbor Selection (FNS) for multimodal optimization. The FNS is aimed for ill-scaled and locally multimodal domain, both found in real-world numerical optimization problem. In FNS, selection is applied to parent-child pair that most likely belong to the same attractor. We determine such pair with statistical comparison of the fitness values sampled from region between the pairs, instead of conventional Euclidean distance. In addition, the ranks of a parent among sampled values are used to determine if the parent is replaceable. These measurements makes the algorithm scaleinvariant thus robust in ill-scaled domain. Categories and subject discriptors: Computing Methodologies[Artificial Intelligence]:Problem Solving, Control Methods, Heuristic Methodsand Search General Terms:Algorithm

• Type: Conference
• Year: 2005
• Where: GECCO
• Authors: Shin Ando, Shigenobu Kobayashi
• Keywords: Fitness-based Neighbor Selection, GECCO 2005, Multimodal Optimization, Real-world Numerical Optimization