Abstract
In this paper, evolutionary algorithms based on probabilistic models (EAPMs) have been recognized as a new computing paradigm in evolutionary computation. There is no traditional crossover or mutation in EAPMs. Instead, they explicitly extract global statistical information from their previous search and build a probability distribution model of promising solutions, based on the extracted information. New solutions are then sampled from the model thus built to replace old solutions. Instances of EAPMs include Population-Based Incremental Learning, the Univariate Marginal Distribution Algorithm (UMDA), Mutual Information Maximization for Input Clustering, the Factorized Distribution Algorithm, the Bayesian Optimization Algorithm, the Learnable Evolution Model and Estimation of Bayesian Networks Algorithms, to name a few. EAPMs have been successfully applied for solving many optimization and search problems.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
