Computers and Operations Research
Evolution of Appropriate Crossover and Mutation Operators in a Genetic Process
Applied Intelligence
Simultaneously Applying Multiple Mutation Operators in Genetic Algorithms
Journal of Heuristics
Theoretical Computer Science - Natural computing
Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Using multiple offspring sampling to guide genetic algorithms to solve permutation problems
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Hybrid Evolutionary Algorithms
Hybrid Evolutionary Algorithms
An improved GA and a novel PSO-GA-based hybrid algorithm
Information Processing Letters
Negative slope coefficient: a measure to characterize genetic programming fitness landscapes
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
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Multiple Offspring Sampling (MOS) is a hybrid algorithm where different evolutionary approaches can coexist simultaneously. The algorithm dynamically evaluates the quality of the solutions produced by each of these algorithms (or techniques, as they are called within MOS) and adjusts their participation in the overall evolutionary process according to this quality value. In this paper we use two alternative measures to evaluate the quality of a reproductive technique and therefore perform the dynamic adjustment of the participation ratios. One of these measures considers the fitness values of the solutions, while the other one determines how difficult the problem is for an evolutionary approach. These two measures are tested and compared over four problems of different complexity and domain (three of them are continuous while the fourth one is discrete). Results show analogies and differences among the used measures and confirm that a good dynamic selection based on a quality measure can boost the performance of the hybrid algorithm.