Probabilistic cooperative-competitive hierarchical modeling as a genetic operator in global optimization

Abstract

Existing search-based discrete global optimization methods share two characteristics: (1) searching at the highest resolution; and (2) searching without memorizing past searching information. In this paper, we firstly provide a model to cope with both. Structurally, it transforms the optimization problem into a selection problem by organizing the continuous search space into a binary hierarchy of partitions. Algorithmically, it is an iterative stochastic cooperative-competitive searching algorithm with memory. It is worth mentioning that the competition model eliminates the requirement of the niche radius required in the existing niching techniques. The model is applied to (but not limited to) function optimization problems (includes high-dimensional problems) with experimental results which show that our model is promising for global optimization. Secondly, we show how pccBHS can be integrated into genetic algorithms as an operator.