Evolutionary Algorithms: The Role of Mutation and Recombination
Evolutionary Algorithms: The Role of Mutation and Recombination
The gambler's ruin problem, genetic algorithms, and the sizing of populations
Evolutionary Computation
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Revisiting evolutionary algorithms with on-the-fly population size adjustment
Proceedings of the 8th annual conference on Genetic and evolutionary computation
FOGA'07 Proceedings of the 9th international conference on Foundations of genetic algorithms
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This paper looks in detail at how an evolutionary algorithm attempts to solve instances from the multimodal problem generator. The paper shows that in order to consistently reach the global optimum, an evolutionary algorithm requires a population size that should grow at least linearly with the number of peaks. A close relationship is also shown between the supply and decision making issues that have been identified previously in the context of population sizing models for additively decomposable problems.The most important result of the paper, however, is that solving an instance of the multimodal problem generator is like solving a peak-in-a-haystack, and it is argued that evolutionary algorithms are not the best algorithms for such a task. Finally, and as opposed to what several researchers have been doing, it is our strong belief that the multimodal problem generator is not adequate for assessing the performance of evolutionary algorithms.