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Chaotic Local Search-Based Differential Evolution Algorithms for Optimization
IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021JADE is a differential evolution (DE) algorithm and has been shown to be very competitive in comparison with other evolutionary optimization algorithms.
Shangce Gao +5 more
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Distributed Differential Evolution With Adaptive Resource Allocation
IEEE Transactions on Cybernetics, 2022Distributed differential evolution (DDE) is an efficient paradigm that adopts multiple populations for cooperatively solving complex optimization problems.
Jian-Yu Li +4 more
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Improving Differential Evolution through Bayesian Hyperparameter Optimization
IEEE Congress on Evolutionary Computation, 2021We propose a novel Evolutionary Algorithm (EA) based on the Differential Evolution algorithm for solving global numerical optimization problem in real-valued continuous parameter space.
Subhodip Biswas +5 more
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Compact Differential Evolution
IEEE Transactions on Evolutionary Computation, 2011This paper proposes the compact differential evolution (cDE) algorithm. cDE, like other compact evolutionary algorithms, does not process a population of solutions but its statistic description which evolves similarly to all the evolutionary algorithms.
Mininno E +3 more
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Q-Learning-based parameter control in differential evolution for structural optimization
Applied Soft Computing, 2021The operations of metaheuristic optimization algorithms depend heavily on the setting of control parameters. Therefore the addition of adaptive control parameter has been widely studied and shown to enhance the problem flexibility and overall performance
Thanh N. Huynh +2 more
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Differential Evolution: A review of more than two decades of research
Engineering applications of artificial intelligence, 2020Since its inception in 1995, Differential Evolution (DE) has emerged as one of the most frequently used algorithms for solving complex optimization problems.
Bilal +4 more
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Differential evolution with adaptive mutation strategy based on fitness landscape analysis
Information Sciences, 2021In recent years, many different differential evolution (DE) variants have been proposed to solve real-world optimization problems. However, the performance of them is largely determined by the selection of the mutation strategy, an approach to choose ...
Zhiping Tan, Kangshun Li, Yi Wang
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Proceedings of the 15th annual conference companion on Genetic and evolutionary computation, 2013
Differential Evolution (DE) is one of the most powerful stochastic real-parameter optimization algorithms of current interest. DE operates through similar computational steps as employed by a standard Evolutionary Algorithm (EA). However, unlike traditional EAs, the DE-variants perturb the current-generation population members with the scaled ...
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Differential Evolution (DE) is one of the most powerful stochastic real-parameter optimization algorithms of current interest. DE operates through similar computational steps as employed by a standard Evolutionary Algorithm (EA). However, unlike traditional EAs, the DE-variants perturb the current-generation population members with the scaled ...
+4 more sources
Binary differential evolution with self-learning for multi-objective feature selection
Information Sciences, 2020Feature selection is an important data preprocessing method. This paper studies a new multi-objective feature selection approach, called the Binary Differential Evolution with self-learning (MOFS-BDE).
Yong Zhang +4 more
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