Investigation of a cellular genetic algorithm that mimics landscape ecology

Abstract

The cellular genetic algorithm (CGA) combines GAs with cellular automata by spreading an evolving population across a pseudo-landscape. In this study we use insights from ecology to introduce new features, such as disasters and connectivity changes, into the algorithm. We investigate the performance and behaviour of the algorithm on standard GA hard problems. The CGA has the advantage of avoiding premature convergence and outperforms standard GAs on particular problems. A potentially important feature of the algorithm’s behaviour is that the fitness of solutions frequently improves in large jumps following disturbances (culling by patches).