Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
SIAM Review
A Taxonomy of Global Optimization Methods Based on Response Surfaces
Journal of Global Optimization
Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Convex Optimization
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Geometric particle swarm optimization for the sudoku puzzle
Proceedings of the 9th annual conference on Genetic and evolutionary computation
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Geometry of evolutionary algorithms
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Geometry of evolutionary algorithms
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Dilemmas in knowledge-based evolutionary computation for financial investing
Intelligent Decision Technologies
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The smoothness of a fitness landscape, to date still an elusive notion, is considered to be a fundamental empirical requirement to obtain good performance for many existing meta-heuristics. In this paper, we suggest that a theory of smooth fitness landscapes is central to bridge the gap between theory and practice in EC. As a first step towards this theory, we formalize the notion of smooth fitness landscapes in a general setting using a Gaussian random field model on metric spaces. Then, for the specific case of the Hamming space, we show experimentally that traditional search algorithms with search operators based on this space reach better performance on smoother fitness landscapes. This shows that the formalized notion of smoothness captures the important heuristic property of its informal counterpart.