Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
Journal of Global Optimization
A computationally efficient evolutionary algorithm for real-parameter optimization
Evolutionary Computation
A comparative study of differential evolution variants for global optimization
Proceedings of the 8th annual conference on Genetic and evolutionary computation
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Accelerating Differential Evolution Using an Adaptive Local Search
IEEE Transactions on Evolutionary Computation
A simple adaptive algorithm for numerical optimization
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Computational Intelligence - Volume Part II
An adaptive single-point algorithm for global numerical optimization
Expert Systems with Applications: An International Journal
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A new evolutionary algorithm, Elitistic Evolution (termed EEv), is proposed in this paper. EEv is an evolutionary method for numerical optimization with adaptive behavior. EEv uses small populations (smaller than 10 individuals). It have an adaptive parameter to adjust the balance between global exploration and local exploitation. Elitism have great influence in EEv' proccess and that influence is also controlled by the adaptive parameter. EEv' crossover operator allows a recently generated offspring individual to be parent of other offspring individuals of its generation. It requires the configuration of two user parameters (many state-of-the-art approaches uses at least three). EEv is tested solving a set of 16 benchmark functions and then compared with Differential Evolution and also with some well-known Memetic Algorithms to show its efficiency. Finally, EEv is tested solving a set of 10 benchmark functions with very high dimensionality (50, 100 and 200 dimensions) to show its robustness.