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
Case-Based Initialization of Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Design of an adaptive mutation operator in an electrical load management case study
Computers and Operations Research
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
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This paper presents a comparative analysis of the results obtained with two different implementations of the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) in the framework of load management activities in power systems. The multiobjective real-world problem deals with the identification and the selection of control strategies to be applied to groups of loads aimed at reducing maximum power demand (PD), maximising profits and minimising user discomfort. It is shown that the algorithm performance is improved when the NSGA-II mutation operator is adaptively changed to incorporate information about the results of the search process and transfer this 'knowledge' to the population.