Real-valued Evolutionary Optimization using a Flexible Probability Density Estimator

Population-Based Incremental Learning (PBIL) is an abstraction of a genetic algorithm , which solves optimization problems by explicitly constructing a probabilistic model of the promising regions of the search space. At each iteration the model is used to generate a population of candidate solutions and is itself modiied in response to these solutions. Through the extension of PBIL to Real-valued search spaces, a more powerful and general algorithmic framework arises which enables the use of arbitrary probability density estimation techniques in evolutionary optimization. To illustrate the usefulness of the framework, we propose and implement an evolutionary algorithm which uses a nite Adaptive Gaussian mixture model density estimator. This method ooers considerable power and exibility in the forms of the density which can be eeectively modeled. We discuss the general applicability of the framework, and suggest that future work should lead to the development of better evolutionary optimization algorithms.