Partnering Strategies for Fitness Evaluation in a Pyramidal Evolutionary Algorithm


Autoria(s): Aickelin, Uwe; Bull, L
Data(s)

2002

Resumo

This paper combines the idea of a hierarchical distributed genetic algorithm with different inter-agent partnering strategies. Cascading clusters of sub-populations are built from bottom up, with higher-level sub-populations optimising larger parts of the problem. Hence higher-level sub-populations search a larger search space with a lower resolution whilst lower-level sub-populations search a smaller search space with a higher resolution. The effects of different partner selection schemes for (sub-)fitness evaluation purposes are examined for two multiple-choice optimisation problems. It is shown that random partnering strategies perform best by providing better sampling and more diversity.

Formato

application/pdf

Identificador

http://eprints.nottingham.ac.uk/255/1/02gecco_partner.pdf

Aickelin, Uwe and Bull, L (2002) Partnering Strategies for Fitness Evaluation in a Pyramidal Evolutionary Algorithm. In: Genetic and Evolutionary Computation Conference, 2002, New York, USA.

Idioma(s)

en

Relação

http://eprints.nottingham.ac.uk/255/

Tipo

Conference or Workshop Item

PeerReviewed