Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Niching methods for genetic algorithms
Niching methods for genetic algorithms
Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Modeling Building-Block Interdependency
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
The role of mutation and recombination in evolutionary algorithms
The role of mutation and recombination in evolutionary algorithms
Linked: How Everything Is Connected to Everything Else and What It Means
Linked: How Everything Is Connected to Everything Else and What It Means
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Problem structure and evolutionary algorithm difficulty
Problem structure and evolutionary algorithm difficulty
A comparison of predictive measures of problem difficulty inevolutionary algorithms
IEEE Transactions on Evolutionary Computation
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The performances (success) of a hill climber RMHC) and a genetic algorithm (upGA) on a set of test problems with varied structural characteristics are compared to learn whether problem structural characteristic can be a feasible solution-independent indicator of when a problem will be more easily solved by a genetic algorithm than by hill climbing. Evidence supporting this hypothesis is found in this initial study. In particular, other factors (modularity, transitivity and fitness distribution) being equal, highly modular problems with broad right-skewed degree distributions are more easily solved by upGA than by RMHC. Suggestions are made for further research in this direction.