Multiple comparison procedures
Multiple comparison procedures
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
A new proposal for multi-objective optimization using differential evolution and rough sets theory
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Design issues in a multiobjective cellular genetic algorithm
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
A review of multiobjective test problems and a scalable test problem toolkit
IEEE Transactions on Evolutionary Computation
jMetal: A Java framework for multi-objective optimization
Advances in Engineering Software
Enhancing the broadcast process in mobile ad hoc networks using community knowledge
Proceedings of the first ACM international symposium on Design and analysis of intelligent vehicular networks and applications
A hybrid cellular genetic algorithm for multi-objective crew scheduling problem
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
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In this work we present a new hybrid cellular genetic algorithm. We take MOCell as starting point, a multi-objective cellular genetic algorithm, and, instead of using the typical genetic crossover and mutation operators, they are replaced by the reproductive operators used in differential evolution. An external archive is used to store the nondominated solutions found during the search process and the SPEA2 density estimator is applied when the archive becomes full. We evaluate the resulting hybrid algorithm using a benchmark composed of three-objective test problems, and we compare the results with several state of the art multi-objective metaheuristics. The obtained results show that our proposal outperforms the other algorithms according to the two considered quality indicators.