The Simple Genetic Algorithm: Foundations and Theory
The Simple Genetic Algorithm: Foundations and Theory
SIAM Review
Theoretical Computer Science - Natural computing
Group properties of crossover and mutation
Evolutionary Computation
Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Structural Search Spaces and Genetic Operators
Evolutionary Computation
Properties of Gray and Binary Representations
Evolutionary Computation
Proceedings of the 8th annual conference on Genetic and evolutionary computation
A comparison of selection schemes used in evolutionary algorithms
Evolutionary Computation
On the futility of blind search: An algorithmic view of “no free lunch”
Evolutionary Computation
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Classes of problems in the black box scenario
Proceedings of the 8th annual conference on Genetic and evolutionary computation
IEEE Transactions on Evolutionary Computation
Dilemmas in knowledge-based evolutionary computation for financial investing
Intelligent Decision Technologies
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Metaheuristics have often been shown to be effective for difficult combinatorial optimization problems. The reason for that, however, remains unclear. A framework for a theory of metaheuristics crucially depends on a formal representative model of such algorithms. This paper unifies/reconciles in a single framework the model of a black box algorithm coming from the no-free-lunch research (e.g. Wolpert et al. [25], Wegener [23]) with the study of fitness landscape. Both are important to the understanding of meta-heuristics, but they have so far been studied separately. The new model is a natural environment to study meta-heuristics.