Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
An overview of parameter control methods by self-adaption in evolutionary algorithms
Fundamenta Informaticae
The theory of evolution strategies
The theory of evolution strategies
Evolutionary Algorithms: The Role of Mutation and Recombination
Evolutionary Algorithms: The Role of Mutation and Recombination
Self-Adaptive Genetic Algorithm for Numeric Functions
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Toward a theory of evolution strategies: On the benefits of sex---the (μ/μ, λ) theory
Evolutionary Computation
Toward a theory of evolution strategies: Self-adaptation
Evolutionary Computation
The equation for response to selection and its use for prediction
Evolutionary Computation
Rigorous hitting times for binary mutations
Evolutionary Computation
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
On self-adaptive features in real-parameter evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Evolutionary Bi-objective Learning with Lowest Complexity in Neural Networks: Empirical Comparisons
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
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In what follows, we propose a new perspective of machine learning into genetic algorithms. The conceptualization of such G-reasoning relies on the semantic of adaptability to tackle efficiently large range of optimization problems. This paper intends to outperform genetic learning according to aβnearest-neighbors selection and a micro-learning schedule. Based upon an adaptation function, the learning behavior put emphasizes on adjustments of mutation rates through generations. Thus, to realize such way, two learning strategies are suggested. Commonly, the aim of this purpose is to regulate the intensity of convergence velocity along of evolution. Indeed, all mentioned requirements influence closely the performance of the algorithm. In addition to the best performance reached, comparisons are done with others evolutionary methods.