Skip Nav Destination
1-1 of 1
Follow your search
Access your saved searches in your account
Would you like to receive an alert when new items match your search?
Publisher: Journals Gateway
Evolutionary Computation (2005) 13 (1): 29–42.
Published: 01 March 2005
AbstractView article PDF
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 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.