Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Niching methods for genetic algorithms
Niching methods for genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Representations for Genetic and Evolutionary Algorithms
Representations for Genetic and Evolutionary Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Efficiency speed-up strategies for evolutionary computation: fundamentals and fast-GAs
Applied Mathematics and Computation
Coding and Information Theory
Neural Network Training Using Genetic Algorithm with a Novel Binary Encoding
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
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In this paper, we introduce a new genetic representation --- a splicing/decomposable (S/D) binary encoding, which was proposed based on some theoretical guidance and existing recommendations for designing efficient genetic representations. Our theoretical and empirical investigations reveal that the S/D binary representation is more proper than other existing binary encodings for searching of genetic algorithms (GAs). Moreover, we define a new genotypic distance on the S/D binary space, which is equivalent to the Euclidean distance on the real-valued space during GAs convergence. Based on the new genotypic distance, GAs can reliably and predictably solve problems of bounded complexity and the methods depended on the Euclidean distance for solving different kinds of optimization problems can be directly used on the S/D binary space.