Abstract
Although real-coded evolutionary algorithms (EAs) have been applied to optimization problems for over thirty years, the convergence properties of these methods remain poorly understood. We discuss the use of discrete random variables to perform search in real-valued EAs. Although most real-valued EAs perform mutation with continuous random variables, we argue that EAs using discrete random variables for mutation can be much easier to analyze. In particular, we present and analyze two simple EAs that make discrete choices of mutation steps.
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Acknowledgments.
We thank John DeLaurentis and Lauren Ferguson for their collaborations on the analysis of self-adaptive EAs, and anonymous reviewers for their critical feedback. This work was performed at Sandia National Laboratories. Sandia is a multiprogram laboratory operated by Sandia corporation, a Lockheed Martin Company, for the United States Department of Energy under Contract DE-AC04-94AL85000.
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Hart, W. Rethinking the design of real-coded evolutionary algorithms: Making discrete choices in continuous search domains. Soft Comput 9, 225–235 (2005). https://doi.org/10.1007/s00500-004-0376-5
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DOI: https://doi.org/10.1007/s00500-004-0376-5