Skip to main content
Article thumbnail
Location of Repository

MODEL-BASED EVOLUTIONARY OPTIMIZATION

By B. Johansson, S. Jain, J. Montoya-torres, J. Hugan, E. Yücesan, Yongqiang Wang, Michael C. Fu and Steven I. Marcus

Abstract

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 Modelbased 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.

Year: 2014
OAI identifier: oai:CiteSeerX.psu:10.1.1.416.922
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://citeseerx.ist.psu.edu/v... (external link)
  • http://www.informs-sim.org/wsc... (external link)
  • Suggested articles


    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.