Analyzing the Performance of Generalized Hill Climbing Algorithms
Journal of Heuristics
Stochastic protein folding simulation in the three-dimensional HP-model
Computational Biology and Chemistry
A study of NK landscapes' basins and local optima networks
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Fitness landscapes and graphs: multimodularity, ruggedness and neutrality
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Fitness landscapes and graphs: multimodularity, ruggedness and neutrality
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Evolutionary algorithm characterization in real parameter optimization problems
Applied Soft Computing
Fitness landscapes and graphs: multimodularity, ruggedness and neutrality
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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The first contribution of this paper is a theoretical investigation of combinatorial optimization problems. Their landscapes are specified by the set of neighborhoods of all points of the search space. The aim of the paper consists of the estimation of the number N of local optima and the distributions of the sizes $(\alpha_j)$ of their attraction basins. For different types of landscapes we give precise estimates of the size of the random sample that ensures that at least one point lies in each attraction basin. A practical methodology is then proposed for identifying these quantities ($N$ and $(\alpha_j)$ distributions) for an unknown landscape, given a random sample of starting points and a local steepest ascent search. This methodology can be applied to any landscape specified with a modification operator and provides bounds on search complexity to detect all local optima. Experiments demonstrate the efficiency of this methodology for guiding the choice of modification operators, eventually leading to the design of problem-dependent optimization heuristics.