Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Global Multiobjective Optimization Using Evolutionary Algorithms
Journal of Heuristics
Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Compaction of Symbolic Layout Using Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
A Variant of Evolution Strategies for Vector Optimization
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
An analysis on recombination in multi-objective evolutionary optimization
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Borg: An auto-adaptive many-objective evolutionary computing framework
Evolutionary Computation
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In this paper we discuss some questions of applying evolutionary algorithms to multiobjective optimization problems with continuous variables. A main question of transforming evolutionary algorithms for scalar optimization into those for multiobjective optimization concerns the modification of the selection step. In an earlier article we have analyzed special properties of selection rules called efficiency preservation and negative efficiency preservation. Here, we discuss the use of these properties by applying an accordingly modified selection rule to some test problems. The number of efficient alternatives of a population for different test problems provides a better understanding of the change of data during the evolutionary process. Also effects of the number of objective functions are treated. We also analyze the influence of the number of objectives and the relevance of these results in the context of the 1/5 rule, a mutation control concept for scalar evolutionary algorithms which cannot easily be transformed into the multiobjective case.