Journal of Global Optimization
Population set-based global optimization algorithms: some modifications and numerical studies
Computers and Operations Research
DE/EDA: a new evolutionary algorithm for global optimization
Information Sciences—Informatics and Computer Science: An International Journal
A Fuzzy Adaptive Differential Evolution Algorithm
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Improved differential evolution via cuckoo search operator
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part I
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Differential Evolution (DE) is a novel evolutionary computation technique, which has attracted much attention and wide applications for its simple concept, easy implementation and quick convergence. In order to enhance the performance of classical DE, a new DE algorithm, namely AMDE, is proposed by using an adaptive mutation. In AMDE, the mutation step size is dynamically adjusted in terms of the size of current search space. To verify the performance of the proposed approach, we test AMDE on six well-known benchmark functions. The simulation results show that AMDE performs better than other three evolutionary algorithms on majority of test functions.