A Hierarchical Clustering Strategy to Improve the Biological Plausibility of an Ecology-Based Evolutionary Algorithm.

It is well known that, in nature, populations are dynamic in space and time. This means that the formation of habitats changes over time and its formation is not deterministic. This work uses the concepts of ecological relationships, ecological successions and probabilistic formation of habitats to build a cooperative search algorithm, named ECO. This work aims at exploring the use of a hierarchical clustering technique to probabilistically set the habitats of the computational ecosystem. The Artificial Bee Colony (ABC) was used in the experiments in which benchmark mathematical functions were optimized. Results were compared with ABC running alone, and the ECO with and without the use of hierarchical clustering. The ECO algorithm with hierarchical clustering performed better than the other approaches, possibly thanks to the ecological interactions (intra and inter-habitats) that enabled the co-evolution of populations and to a more bio-plausible probabilistic strategy for habitats definition. Also, a critical parameter was suppressed.