A Simple Cellular Genetic Algorithm for Continuous Optimization

Cellular genetic algorithms (cGAs) are a kind of genetic algorithm (GA) -population based heuristic-with a structured population so that individuals can only interact with their neighbors. The existence of small overlapped neighborhoods in this decentralized population provides both diversity and exploration, while the exploitation of the search space is strengthened inside each neighborhood. This balance between exploration and exploitation makes cGAs naturally suitable for solving complex problems. In this paper we tackle the minimization of a number of problems (both academic and from the real world) with a real-coded cGA, called JCell. The results show that JCell improves the compared algorithms for a number of the studied problems, thus increasing the overall performance with respect to other complex heterogeneous distributed GAs, belonging to the state-of-the-art in continuous optimization.