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A Simplified Artificial Life Model for Multiobjective Optimisation: A Preliminary Report


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Berry, A and Vamplew, P 2003 , 'A Simplified Artificial Life Model for Multiobjective Optimisation: A Preliminary Report', paper presented at the 2003 IEEE Congress on Evolutionary Computation, 8-12 December, 2003, Canberra, Australia.

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Recent research in the field of Multiobjective
Optimisation (MOO) has been focused on achieving the
Pareto optimal front by explicitly analysing the
dominance level of individual solutions. While such
approaches have produced good results for a variety of
problems, they are computationally expensive due to the
complexities of deriving the dominance level for each
solution against the entire population. TB_MOO
(Threshold Based Multiobjective Optimisation) is a new
artificial life approach to MOO problems that does not
analyse dominance, nor perform any agent-agent
comparisons. This reduction in complexity results in a
significant decrease in processing overhead. Results
show that TB_MOO performs comparably, and often
better, than its more complicated counter-parts with
respect to distance from the Pareto optimal front, but is
slightly weaker in terms of distribution and extent.

Item Type: Conference or Workshop Item (Paper)
Authors/Creators:Berry, A and Vamplew, P
Keywords: multi-objective optimisation, artificial life
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