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Population-Based Continuous Optimization, Probabilistic Modelling and Mean Shift
2005
Evolutionary Computation
Evolutionary algorithms perform optimization using a population of sample solution points. An interesting development has been to view population-based optimization as the process of evolving an explicit, probabilistic model of the search space. This paper investigates a formal basis for continuous, population-based optimization in terms of a stochastic gradient descent on the Kullback-Leibler divergence between the model probability density and the objective function, represented as an unknown
doi:10.1162/1063656053583478
pmid:15901425
fatcat:qfrjl2sw7rfqlgddfzk24jq7tq