Cellular probabilistic evolutionary algorithms for real-coded function optimization

Abstract

We propose a novel Cellular Probabilistic Evolutionary Algorithm (CPEA) based on a probabilistic representation of solutions for real coded problems. In place of binary integers, the basic unit of information here is a probability density function. This probabilistic coding allows superposition of states for a more efficient algorithm. Furthermore, the cellular structure of the proposed algorithm aims to provide an appropriate tradeoff between exploitation and exploration. Experimental results show that the performance of CPEA in several numerical benchmark problems is improved when compared with other evolutionary algorithms like Particle Swarm Optimization (PSO) and Genetic Algorithms (GA). © 2008 Springer-Verlag.