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Research On Genetic Algorithm Based On Fixed Point Theory

Posted on:2011-03-07Degree:MasterType:Thesis
Country:ChinaCandidate:Y M ShangFull Text:PDF
GTID:2178330332470188Subject:Computer application technology
Abstract/Summary:PDF Full Text Request
In this paper an improved genetic algorithm( IGA) is proposed to solve optimal problems based on subdivision theory and fixed point algorithms of continuous self-mapping in Euclidean space, and on this basis, two models of genetic algorithm respect to single-objective optimization and multi-objective optimization problems have been proposed. Both genetic algorithm models operate on a simplicial subdivision of searching space and generate the integer labels at the vertices, and then crossover operators and increasing dimension operators relying on the integer labels are designed. In this case, whether every individual of the population is a completely labeled simplex can be used as an objective convergence criterion and that determined whether the algorithm will be terminated. On the design of model for multi-objective optimization, exclusive method is used for constructing non-dominated solutions, and adopting elites to keep the tactics, chosing a few of individuals from the non-dominated solutions to generate the next population.The two algorithm models combined genetic algorithm with fixed point algorithms and triangulation theory to maintain the proper diversity, stability and convergence of the population. And several numerical examples are provided to be examined and the results show that the model for single-objective optimization have higher global optimization capability, computing efficiency and strong stability than traditional numerical optimization methods and standard genetic algorithms. Simultaneously, the multi-objective model can converge to pareto solutions quickly and obtained uniform distribution.
Keywords/Search Tags:Genetic Algorithm, Fixed Point, Simplicial Triangulation, Integer Label, Pareto Solutions, Pareto Front
PDF Full Text Request
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