Bayesian evolutionary algorithms for continuous function optimization

Recently many researchers have studied the estimation of distribution algorithms (EDAs) as an optimization method. While most EDAs focus on solving combinatorial optimization problems, only a few algorithms have been proposed for continuous function optimization. In previous work, we developed a Bayesian evolutionary algorithm (BEA) for combinatorial optimization problems using a probabilistic graphical model known as a Helmholtz machine. Since BEA is a general framework for evolutionary computation based on the Bayesian inductive principle, we improved BEA for continuous function optimization problems. By using the nature of the neural network and availability of the wake-sleep learning algorithm, the Helmholtz machine can capture the continuous distribution with a small modification. The proposed method has been applied to a suite of benchmark functions and compared with a real-coded genetic algorithm and previous experimental results