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Representation Development from Pareto-Coevolution
Edwin D. de Jong
DSS Group,
Utrecht University, The Netherlands
dejong@cs.uu.nl,
http://www.cs.uu.nl/~dejong/
Abstract. Genetic algorithms generally use a fixed problem representation that maps variables of the search space to variables of the problem, and operators of variation that are fixed over time. This limits their scalability on non-separable problems. To address this issue, methods have been proposed that coevolve explicitly represented modules. An open question is how modules in such coevolutionary setups should be evaluated.
Recently, Pareto-coevolution has provided a theoretical basis for
evaluation in coevolution. We define a notion of functional
modularity, and objectives for module evaluation based on
Pareto-Coevolution. It is shown that optimization of these
objectives maximizes functional modularity. The resulting
evaluation method is developed into an algorithm for variable
length, open ended development of representations called
DevRep. DevRep successfully identifies large partial solutions
and greatly outperforms fixed length and variable length genetic
algorithms on several test problems, including the 1024-bit
Hierarchical-XOR problem.
Keywords: Development of representations, hierarchical modularity, Pareto-coevolution, Evolutionary Multi-Objective Optimization
LNCS 2723, p. 262 ff.
lncs@springer.de
© Springer-Verlag Berlin Heidelberg 2003