Proceedings of the third international conference on Genetic algorithms
An introduction to differential evolution
New ideas in optimization
Journal of Global Optimization
The Effects of Control Parameters and Restarts on Search Stagnation in Evolutionary Programming
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Raising Theoretical Questions About the Utility of Genetic Algorithms
EP '97 Proceedings of the 6th International Conference on Evolutionary Programming VI
A comparative study of differential evolution variants for global optimization
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Solving rotated multi-objective optimization problems using differential evolution
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Improving the Performance and Scalability of Differential Evolution
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
Influence of crossover on the behavior of Differential Evolution Algorithms
Applied Soft Computing
Free search differential evolution
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
CODEQ: an effective metaheuristic for continuous global optimisation
International Journal of Metaheuristics
Adaptive differential evolution with optimization state estimation
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Principal coordinate strategy: a novel adaptive control strategy for differential evolution
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Information Sciences: an International Journal
Evolutionary algorithm characterization in real parameter optimization problems
Applied Soft Computing
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Recent results show that the Differential Evolution algorithm has significant difficulty on functions that are not linearly separable. On such functions, the algorithm must rely primarily on its differential mutation procedure which, unlike its recombination strategy, is rotationally invariant. We conjecture that this mutation strategy lacks sufficient selective pressure when appointing parent and donor vectors to have satisfactory exploitative power on non-separable functions. We find that imposing pressure in the form of rank-based differential mutation results in a significant improvement of exploitation on rotated benchmarks.