Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Epistasis in Genetic Algorithms: An Experimental Design Perspective
Proceedings of the 6th International Conference on Genetic Algorithms
Fitness Landscapes and Evolutionary Algorithms
AE '99 Selected Papers from the 4th European Conference on Artificial Evolution
Mutation-crossover isomorphisms and the construction of discriminating functions
Evolutionary Computation
Algebraic theory of recombination spaces
Evolutionary Computation
Non-parametric Estimation of Properties of Combinatorial Landscapes
Proceedings of the Applications of Evolutionary Computing on EvoWorkshops 2002: EvoCOP, EvoIASP, EvoSTIM/EvoPLAN
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Kauffman's N K-landscapes have become a popular tool for investigating properties of heuristic search algorithms. In this paper we carry out some experiments with a more general, but still tuneable, class of landscapes which we call l, θ landscapes. These landscapes are characterized by a parameter θ which allows interactions at all orders, rather than merely at orders up to a fixed level as is the case with N K-landscapes. This is accomplished by fixing the magnitude and sign of the effects in an experimental design (ED) decomposition of a function. In some cases the epistasis variance is a simple funclioii of θ, and can be specified in advance. Further, by choosing some measure of the Hamming landscape associated with these functions, such as the number of local optima or the size of the global optimum's basin, it is possible to tune the landscape by mapping the effects onto a search problem. Some experiments are reported with a GA on these landscapes, with results that are rather surprising, in that the quality of the solution obtained appears to be poorly predicted by the properties of the associated Hamming landscape.