Efficient and Accurate Parallel Genetic Algorithms
Efficient and Accurate Parallel Genetic Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Fine-Grained Parallel Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
A Spatial Predator-Prey Approach to Multi-objective Optimization: A Preliminary Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Spatially Structured Evolutionary Algorithms: Artificial Evolution in Space and Time (Natural Computing Series)
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Inside a predator-prey model for multi-objective optimization: a second study
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
A scalable multi-objective test problem toolkit
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
New ideas in applying scatter search to multiobjective optimization
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
A multi-objective tabu search algorithm for constrained optimisation problems
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Parallelism and evolutionary algorithms
IEEE Transactions on Evolutionary Computation
The exploration/exploitation tradeoff in dynamic cellular genetic algorithms
IEEE Transactions on Evolutionary Computation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Solving Three-Objective Optimization Problems Using a New Hybrid Cellular Genetic Algorithm
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
A Study of Convergence Speed in Multi-objective Metaheuristics
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
A study of multiobjective metaheuristics when solving parameter scalable problems
IEEE Transactions on Evolutionary Computation
A study of the bi-objective next release problem
Empirical Software Engineering
Using multi-objective metaheuristics to solve the software project scheduling problem
Proceedings of the 13th annual conference on Genetic and evolutionary computation
jMetal: A Java framework for multi-objective optimization
Advances in Engineering Software
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
Robust solutions for the software project scheduling problem: a preliminary analysis
International Journal of Metaheuristics
Parallel predator---prey interaction for evolutionary multi-objective optimization
Natural Computing: an international journal
Evolutionary algorithms for the multi-objective test data generation problem
Software—Practice & Experience
Computational Optimization and Applications
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In this paper we study a number of issues related to the design of a cellular genetic algorithm (cGA) for multiobjective optimization. We take as an starting point an algorithm following the canonical cGA model, i.e., each individual interacts with those ones belonging to its neighborhood, so that a new individual is obtained using the typical selection, crossover, and mutation operators within this neighborhood. An external archive is used to store the non-dominated solutions found during the evolution process. With this basic model in mind, there are many different design issues that can be faced. Among them, we focus here on the synchronous/asynchronous feature of the cGA, the feedback of the search experience contained in the archive into the algorithm, and two different replacement strategies. We evaluate the resulting algorithms using a benchmark of problems and compare the best of them against two state-of-the-art genetic algorithms for multiobjective optimization. The obtained results indicate that the cGA model is a promising approach to solve this kind of problem.