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Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces
It is demonstrated that the new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous spacefunctions Converges faster and with more certainty than manyother acclaimed global optimization methods. Expand
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Problems demanding globally optimal solutions are ubiquitous, yet many are intractable when they involve constrained functions having many local optima and interacting, mixed-type variables.TheExpand
An introduction to differential evolution
Real-parameter optimization with differential evolution
This study reports how the differential evolution (DE) algorithm performed on the test bed developed for the CEC05 contest for real parameter optimization. The test bed includes 25 scalableExpand
Minimizing the real functions of the ICEC'96 contest by differential evolution
  • R. Storn, K. Price
  • Mathematics, Computer Science
  • Proceedings of IEEE International Conference on…
  • 20 May 1996
Two variants of DE are described which were used to minimize the real test functions of the ICEC'96 contest. Expand
New ideas in optimization
The techniques treated in this text represent research as elucidated by the leaders in the field and are applied to real problems, such as hilllclimbing, simulated annealing, and tabu search. Expand
Differential evolution: a fast and simple numerical optimizer
  • K. Price
  • Computer Science
  • Proceedings of North American Fuzzy Information…
  • 19 June 1996
The performance of DE on a testbed of 15 functions is compared with a variety of recently published results encompassing many different methods and DE converged for all 15 functions and was the fastest method for solving 11 of them. Expand
Differential evolution vs. the functions of the 2/sup nd/ ICEO
  • K. Price
  • Mathematics
  • Proceedings of IEEE International Conference on…
  • 13 April 1997
Differential evolution (DE) is a simple evolutionary algorithm for numerical optimization whose most novel feature is that it mutates vectors by adding weighted, random vector differentials to them.Expand
Eliminating Drift Bias from the Differential Evolution Algorithm
A new, rotationally invariant DE algorithm is presented that eliminates drift bias from its trial vector generating function by projecting randomly chosen vector differences along lines of recombination, leaving recombination as the only migration pathway. Expand