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In order to overcome the shortcomings of Immune Genetic Algorithm's relatively slow convergence rate during the process of solving large-scale optimization problem, given Chaos optimization's benefits of sensitive to the initial value, easy to jump out of local minimum point, the fast search speed, global asymptotic convergence and so on, basing on the both search advantages of Immune Evolutionary...
Adaptive genetic algorithm for solving job-shop scheduling problems has the defects of the slow convergence speed on the early stage and it is easy to trap into local optimal solutions, this paper introduces a time operator depending on the time evolution to solve this problem. Its purpose is to overcome the defect of adaptive genetic algorithm whose crossover and mutation probability can not make...
Proposing a new algorithm which is simple but effective. Using characteristic of biological evolution and common sense to design the selection operator, improve the variation method of the crossover probability and the mutation probability. Numerical experiments show that the new algorithm is more effective than the comparative algorithm in realizing the high convergence speed, convergence precision,...
This paper focuses on the discussion about the briefest reduct of Rough Sets which is extracted by genetic algorithm. The fitting function is designed by the combination of the relying degree of RS and sum of seeds which is the attributes of data. Genetic Algorithm operator is applied and the algorithm is tested by UCI database. After the analysis and discussion, RGA and RGA_2 have been proved available...
To solve two problems of niche genetic algorithm: premature convergence and lower convergence,a adaptive isolation niche genetic algorithm is proposed. The algrorithm forms niches based on isolation which well keeps diversities of the population,and designs adaptive operator according to generation. Crossover probability and mutation probability adjust to the evolution, which will accelerate convergence...
An adaptive population-based incremental learning algorithm (APBIL) is presented basing on analyzing the characteristics of traditional PBIL algorithm in this paper. Overcoming disadvantages of traditional PBIL algorithm, the proposed APBIL algorithm can adjust learning rate and mutation probability automatically according to the evolution degree of the algorithm's searching performed. Extensive computational...
The genetic algorithm is a powerful method to analyze many complex issue, especially in the optimization problems. The main challenges of genetic algorithm are premature convergence on local minimum and long convergence time. In this paper, a new genetic algorithm, named partial mutation in GA (PMGA) is proposed for tackling of these problems. PMGA is using elitism selection and improved mutation...
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