An Empirical Analysis on Dimensionality in Cellular Genetic Algorithms

Abstract

Cellular or fine grained Genetic Algorithms (GAs) are a massively parallel algorithmic approach to GAs. Decentralizing their population allows alternative ways to explore and to exploit the solutions landscape. Individuals interact locally through nearby neighbours while the entire population is globally exploring the search space throughout a predefined population's topology. Having a decentralized population requires the definition of other algorithmic configuration parameters; such as shape and number of individuals within the local neighbourhood, population's topology shape and dimension, local instead of global selection criteria, among others. In this article, attention is paid to the population's topology dimension in cGAs. Several benchmark problems are assessed for 1, 2, and 3 dimensions while combining a local selection criterion that significantly affect overall selective pressure. On the other hand, currently available high performance processing platforms such as Field Programmable Gate Arrays (FPGAs) and Graphics Processing Units (GPUs) offer massively parallel fabrics. Therefore, having a strong empirical base to understand structural properties in cellular GAs would allow to combine physical properties of these platforms when designing hardware architectures to tackle difficult optimization problems where timing constraints are mandatory.