Punctuated equilibria: a parallel genetic algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Genotypic and phenotypic assortative mating in genetic algorithm
Information Sciences: an International Journal
Using assortative mating in genetic algorithms for vector quantization problems
Proceedings of the 2001 ACM symposium on Applied computing
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
An Investigation of Niche and Species Formation in Genetic Function Optimization
Proceedings of the 3rd International Conference on Genetic Algorithms
Fine-Grained Parallel Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
When Selection Meets Seduction
Proceedings of the 6th International Conference on Genetic Algorithms
Intelligent behavior as an adaptation to the task environment
Intelligent behavior as an adaptation to the task environment
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
On the harmonious mating strategy through tabu search
Information Sciences: an International Journal - Special issue: Evolutionary computation
Embodiment of Evolutionary Computation in General Agents
Evolutionary Computation
Paper: The parallel genetic algorithm as function optimizer
Parallel Computing
Mating restriction and niching pressure: results from agents and implications for general EC
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Assortative mating in genetic algorithms for dynamic problems
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Adaptive generalized crowding for genetic algorithms
Information Sciences: an International Journal
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Genetic algorithms typically use crossover, which relies on mating a set of selected parents. As part of crossover, random mating is often carried out. A novel approach to parent mating is presented in this work. Our novel approach can be applied in combination with a traditional similarity-based criterion to measure distance between individuals or with a fitness-based criterion. We introduce a parameter called the mating index that allows different mating strategies to be developed within a uniform framework: an exploitative strategy called best-first, an explorative strategy called best-last, and an adaptive strategy called self-adaptive. Self-adaptive mating is defined in the context of the novel algorithm, and aims to achieve a balance between exploitation and exploration in a domain-independent manner. The present work formally defines the novel mating approach, analyzes its behavior, and conducts an extensive experimental study to quantitatively determine its benefits. In the domain of real function optimization, the experiments show that, as the degree of multimodality of the function at hand grows, increasing the mating index improves performance. In the case of the self-adaptive mating strategy, the experiments give strong results for several case studies.