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Journal Articles Memetic Computing Year : 2012

A benchmark for cooperative coevolution

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Abstract

Cooperative co-evolution algorithms (CCEA) are a thriving sub-field of evolutionary computation. This class of algorithms makes it possible to exploit more efficiently the artificial Darwinist scheme, as soon as an optimisation problem can be turned into a co-evolution of interdependent sub-parts of the searched solution. Testing the efficiency of new CCEA concepts, however, it is not straightforward: while there is a rich literature of benchmarks for more traditional evolutionary techniques, the same does not hold true for this relatively new paradigm. We present a benchmark problem designed to study the behavior and performance of CCEAs, modeling a search for the optimal placement of a set of lamps inside a room. The relative complexity of the problem can be adjusted by operating on a single parameter. The fitness function is a trade-off between conflicting objectives, so the performance of an algorithm can be examined by making use of different metrics. We show how three different cooperative strategies, Parisian Evolution, Group Evolution and AllopatricGroup Evolution, can be applied to the problem. Using a Classical Evolution approach as comparison, we analyse the behavior of each algorithm in detail, with respect to the size of the problem.
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Dates and versions

hal-00758187 , version 1 (29-11-2012)

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Alberto Tonda, Evelyne Lutton, Giovanni Squillero. A benchmark for cooperative coevolution. Memetic Computing, 2012, 4 (4), pp.263-277. ⟨10.1007/s12293-012-0095-x⟩. ⟨hal-00758187⟩
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