Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Cryptanalysis of Number Theoretic Ciphers
Cryptanalysis of Number Theoretic Ciphers
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
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Genetic algorithm (GA) is a well known algorithm applied to a wide variety of optimization problems [4]. It combines selection, crossover, and mutation operators in order to find the best solution to a problem. The standard GA operates on chromosomes represented by binary code strings [1, 2]. This paper designs alternative operators in the GA process. The new operations reduce the binary decoding process of chromosomes when performing the computation. Variations of solutions with the implemented operations on chromosomes are studied. Computational examples show that the new methods save the computer time and enhance the efficiency when compared to the standard GA.