An investigation of niche and species formation in genetic function optimization
Proceedings of the third international conference on Genetic algorithms
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Noise, sampling, and efficient genetic algorthms
Noise, sampling, and efficient genetic algorthms
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
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
A Mathematical Analysis of Tournament Selection
Proceedings of the 6th International Conference on Genetic Algorithms
Genetic algorithms, selection schemes, and the varying effects of noise
Evolutionary Computation
Running time analysis of multiobjective evolutionary algorithms on pseudo-Boolean functions
IEEE Transactions on Evolutionary Computation
Intelligent evolutionary algorithms for large parameter optimization problems
IEEE Transactions on Evolutionary Computation
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A quality-time analysis of multi-objective evolutionary algorithms (MOEAs) based on schema theorem and building blocks hypothesis is developed. A bicriteria OneMax problem, a hypothesis of niche and species, and a definition of dissimilar schemata are introduced for the analysis. In this paper, the convergence time, the first and last hitting time models are constructed for analyzing the performance of MOEAs. Population sizing model is constructed for determining appropriate population sizes. The models are verified using the bicriteria OneMax problem. The theoretical results indicate how the convergence time and population size of a MOEA scale up with the problem size, the dissimilarity of Pareto-optimal solutions, and the number of Pareto-optimal solutions of a multi-objective optimization problem.