Population-Based Continuous Optimization, Probabilistic Modelling and Mean Shift

Marcus Gallagher, Marcus Frean
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
more » ... density of assumed form. This leads to an update rule that is related and compared with previous theoretical work, a continuous version of the population-based incremental learning algorithm, and the generalized mean shift clustering framework. Experimental results are presented that demonstrate the dynamics of the new algorithm on a set of simple test problems.
doi:10.1162/1063656053583478 pmid:15901425 fatcat:qfrjl2sw7rfqlgddfzk24jq7tq