A connectionist machine for genetic hillclimbing
A connectionist machine for genetic hillclimbing
An analysis of the effects of selection in genetic algorithms
An analysis of the effects of selection in genetic algorithms
Proceedings of the third international conference on Genetic algorithms
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Genetic Algorithms in Search, Optimization and Machine Learning
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
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Evolutionary Computation
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Evolutionary Computation
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EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
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EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
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IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Rigorous analyses of simple diversity mechanisms
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Theoretical analysis of diversity mechanisms for global exploration
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Comparison of simple diversity mechanisms on plateau functions
Theoretical Computer Science
Computers and Operations Research
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Evolutionary Computation
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Applied Soft Computing
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This paper covers an investigation on the effects of diversity control in the search performances of single-objective and multi-objective genetic algorithms. The diversity control is achieved by means of eliminating duplicated individuals in the population and dictating the survival of non-elite individuals via either a deterministic or a stochastic selection scheme. In the case of single-objective genetic algorithm, onemax and royal road R 1 functions are used during benchmarking. In contrast, various multi-objective benchmark problems with specific characteristics are utilised in the case of multi-objective genetic algorithm. The results indicate that the use of diversity control with a correct parameter setting helps to prevent premature convergence in single-objective optimisation. Furthermore, the use of diversity control also promotes the emergence of multi-objective solutions that are close to the true Pareto optimal solutions while maintaining a uniform solution distribution along the Pareto front.