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Improving metaheuristic performance by evolving a variable fitness function
Dahal, Keshav P. Remde, Stephen M. Cowling, Peter I. Colledge, N.J.
Dahal, Keshav P.
Remde, Stephen M.
Cowling, Peter I.
Colledge, N.J.
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Publication Date
2008
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© 2008 Springer Verlag. Reproduced in accordance with the publisher's self-archiving policy. Original publication is available at http://www.springerlink.com
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openAccess
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Abstract
In this paper we study a complex real world workforce scheduling
problem. We apply constructive search and variable neighbourhood search
(VNS) metaheuristics and enhance these methods by using a variable fitness
function. The variable fitness function (VFF) uses an evolutionary approach to
evolve weights for each of the (multiple) objectives. The variable fitness
function can potentially enhance any search based optimisation heuristic where
multiple objectives can be defined through evolutionary changes in the search
direction. We show that the VFF significantly improves performance of
constructive and VNS approaches on training problems, and "learn" problem
features which enhance the performance on unseen test problem instances.
Version
Accepted manuscript
Citation
Dahal KP, Remde SM, Cowling PI et al (2008) Improving metaheuristic performance by evolving a variable fitness function. In: Evolutionary computation in combinatorial optimization. 8th European Conference (EvoCOP 2008) Naples, Italy, March 26-28, 2008: 170-181.
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Type
Conference paper