Skip navigation
PDF (706 KB)
DOI: http://dx.doi.org/10.7551/978-0-262-31050-5-ch024
Pages 171-177
First published 2 July 2012

An ecology-based evolutionary algorithm to evolve solutions to complex problems

Sherri Goings, Heather Goldsby, Betty H.C. Cheng, Charles Ofria

Abstract

Evolutionary algorithms have shown great promise in evolving novel solutions to real-world problems, but the complexity of those solutions is limited, unlike the apparently open-ended evolution that occurs in the natural world. In part, nature surmounts these complexity barriers with ecological dynamics that generate a diverse array of raw materials for evolution to build upon. The authors previously introduced Eco-EA, an evolutionary algorithm that integrates these natural ecological dynamics to promote and maintain diversity in the evolving population. Here, we apply the Eco-EA to the real-world software engineering problem of evolving behavioral models for deployed nodes in a remote sensor network for flood monitoring. We show that the Eco-EA evolves good behavioral models faster than a traditional EA, generates a more diverse suite of models than a traditional EA, and creates models that are themselves more evolvable than those created by a traditional EA.