From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
A Fictitious Play Approach to Large-Scale Optimization
Operations Research
What evolutionary game theory tells us about multiagent learning
Artificial Intelligence
A particle filtering framework for randomized optimization algorithms
Proceedings of the 40th Conference on Winter Simulation
A Model Reference Adaptive Search Method for Global Optimization
Operations Research
Dynamic sample budget allocation in model-based optimization
Journal of Global Optimization
On the convergence of a class of estimation of distribution algorithms
IEEE Transactions on Evolutionary Computation
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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.