Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
How to Solve It: Modern Heuristics
How to Solve It: Modern Heuristics
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
A Fuzzy Adaptive Differential Evolution Algorithm
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
A constraint handling approach for the differential evolution algorithm
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Advances in Differential Evolution
Advances in Differential Evolution
Constraint-Handling in Evolutionary Optimization
Constraint-Handling in Evolutionary Optimization
IEEE Transactions on Evolutionary Computation
An Adaptive Tradeoff Model for Constrained Evolutionary Optimization
IEEE Transactions on Evolutionary Computation
Biogeography-based optimization with blended migration for constrained optimization problems
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Differential evolution in constrained numerical optimization: An empirical study
Information Sciences: an International Journal
Differential evolution algorithm with ensemble of parameters and mutation strategies
Applied Soft Computing
Blended biogeography-based optimization for constrained optimization
Engineering Applications of Artificial Intelligence
C-strategy: a dynamic adaptive strategy for the CLONALG algorithm
Transactions on computational science VIII
C-strategy: a dynamic adaptive strategy for the CLONALG algorithm
Transactions on computational science VIII
Hi-index | 0.01 |
In this paper we present the addition of parameter control in a Differential Evolution algorithm for constrained optimization. Three parameters are self-adapted by encoding them within each individual and a fourth parameter is controlled by a deterministic approach. A set of experiments are performed in order (1) to determine the performance of the modified algorithm with respect to its original version, (2) to analyze the behavior of the self-adaptive parameter values and (3) to compare it with respect to state-of-the-art approaches. Based on the obtained results, some findings regarding the values for the DE parameters as well as for the parameters related with the constraint-handling mechanism are discussed.