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ADAPTIVE LEARNING SEARCH, A NEW TOOL TO HELP COMPREHENDING METAHEURISTICS
2007
International journal on artificial intelligence tools
The majority of the algorithms used to solve hard optimization problems today are population metaheuristics. These methods are often presented under a purely algorithmic angle, while insisting on the metaphors which led to their design. We propose in this article to regard population metaheuristics as methods making evolution a probabilistic sampling of the objective function, either explicitly, implicitly, or directly, via processes of learning, diversification, and intensification. We present
doi:10.1142/s0218213007003370
fatcat:zmhfzs6sjvgpbmvxcail6nu24i