Model-based Evolutionary Optimization

We propose a new framework for global optimization by building a connection between global optimization problems and evolutionary games. Based on this connection, we propose a Model-based Evolutionary Optimization (MEO) algorithm, which uses probabilistic models to generate new candidate solutions and uses various dynamics from evolutionary game theory to govern the evolution of the probabilistic models. The MEO algorithm also gives new insight into the mechanism of model updating in model-based global optimization algorithms. Based on the MEO algorithm, a novel Population Model-based Evolutionary Optimization (PMEO) algorithm is proposed, which better captures the multimodal property of global optimization problems and gives better simulation results.