A genetic algorithm for flowshop sequencing
Computers and Operations Research - Special issue on genetic algorithms
How to solve it: modern heuristics
How to solve it: modern heuristics
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
On the Performance Assessment and Comparison of Stochastic Multiobjective Optimizers
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
Evolutionary computation: comments on the history and current state
IEEE Transactions on Evolutionary Computation
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Improving the responsiveness of NSGA-II using an adaptive mutation operator: a case study
International Journal of Advanced Intelligence Paradigms
Hi-index | 0.01 |
An adequately designed and parameterized set of operators is crucial for an efficient behaviour of Genetic Algorithms (GAs). Several strategies have been adopted in order to better adapt parameters to the problem under resolution and to increase the algorithm's performance. One of these approaches consists in using operators presenting a dynamic behaviour, that is displaying a different qualitative behaviour in different stages of the evolutionary process. In this work a comparative analysis of the effects of using an adaptive mutation operator is presented in the operational framework of a multi-objective GA for the design and selection of electrical load management strategies. It is shown that the use of a time/space varying mutation operator depending on the values achieved for each objective function increases the performance of the algorithm.