Population resizing using nonlinear dynamics in an ecology-based approach

Authors:
Rafael Stubs Parpinelli;Heitor Silvério Lopes
Affiliations:
Applied Cognitive Computing Group, Santa Catarina State University, Joinville, Brazil,Bioinformatics Laboratory, Federal Technological University of Paraná, Curitiba, Brazil;Bioinformatics Laboratory, Federal Technological University of Paraná, Curitiba, Brazil
Venue:
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Year:
2012

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Abstract

It is well known that, in nature, populations are dynamic in space and time. This means that the size of populations oscillate across their habitats over time. This work uses the concepts of habitats, ecological relationships, ecological successions and population dynamics to build a cooperative search algorithm, named ECO. This work aims to explore the population sizing not as a parameter but as a dynamic process. The Artificial Bee Colony (ABC) was used in the experiments where benchmark mathematical functions were optimized. Results were compared with ABC running alone, with and without the use of population dynamics. The ECO algorithm with population dynamics 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 natural survival selection mechanism by the use of population dynamics.