Proceedings of the fourth international conference on Genetic algorithms
Proceedings of the fourth international conference on Genetic algorithms
Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Finding Multimodal Solutions Using Restricted Tournament Selection
Proceedings of the 6th International Conference on Genetic Algorithms
Improving GA search reliability using maximal hyper-rectangle analysis
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Parameter Mapping: A genetic programming approach to function optimization
International Journal of Knowledge-based and Intelligent Engineering Systems - Genetic Programming An Emerging Engineering Tool
The crowding approach to niching in genetic algorithms
Evolutionary Computation
A `nondecimated' lifting transform
Statistics and Computing
Pattern Recognition Letters
Information Sciences: an International Journal
Rotationally invariant crossover operators in evolutionary multi-objective optimization
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
Land combat scenario planning: a multiobjective approach
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
Diversity and multimodal search with a hybrid two-population GA: an application to ANN development
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Adaptive generalized crowding for genetic algorithms
Information Sciences: an International Journal
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The aim of this paper is to identify Genetic Algorithms (GAs) which perform well over a range of continuous and smooth multimodal real-variable functions. In our study, we focus on testing GAs combining three classes of genetic operators: selection, crossover and replacement. The approach followed is time-constrained and thus our stopping criterion is a fixed number of generations. Results show that GAs with random selection of parents and crowding replacement are robust optimizers. By contrast, GAs with tournament selection of parents and random replacement perform poorly in comparison.