Journal Article10.1016/J.INS.2018.04.024
An improved differential evolution algorithm using efficient adapted surrogate model for numerical optimization
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TL;DR: An effective surrogate model is introduced to assist the differential evolution algorithm to generate competitive solutions during the search process and the simulation results indicate that the new technique can improve the performance to generate better statistical significance solutions.
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About: This article is published in Information Sciences. The article was published on 01 Jul 2018. The article focuses on the topics: Surrogate model & Optimization problem.
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TL;DR: Introduction PART I: A COMPARISON of EVOLUTIONARY ALGORITHMS 1. Organic Evolution and Problem Solving 2. Specific Evolutionary Algorithms 3. Artificial Landscapes 4. An Empirical Comparison 5. Selection 6. Mutation 7. An Experiment in Meta-Evolution
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Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization
TL;DR: This paper proposes a self- Adaptive DE (SaDE) algorithm, in which both trial vector generation strategies and their associated control parameter values are gradually self-adapted by learning from their previous experiences in generating promising solutions.
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