Performance Appraisement of the Simulated Annealing Genetic Algorithms
FU Yong-feng
Abstract
FU Yong-feng
Abstract
Traditional Genetic algorithm have two serious shortcomings,namely can't overcame and restrained the phenomenon for a long time effectively,And is evolving on later stage and searching for efficiency relatively low. Simulation anneal algorithm to set up a kind of the overall situation that stand up optimize the method most on the basis of mechanism that the metal anneals, It can be in order to search for small spot the most of the overall situation that technology finds the function of targets from meaning of probability at random. This text anneal Genetic algorithm and simulation algorithm combine together,propose the simulated annealing Genetic algorithm. The experimental result shows, can their is greater improvement on performance in this algorithm.
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Traditional Genetic algorithm have two serious shortcomings,namely can't overcame and restrained the phenomenon for a long time effectively,And is evolving on later stage and searching for efficiency relatively low. Simulation anneal algorithm to set up a kind of the overall situation that stand up optimize the method most on the basis of mechanism that the metal anneals, It can be in order to search for small spot the most of the overall situation that technology finds the function of targets from meaning of probability at random. This text anneal Genetic algorithm and simulation algorithm combine together,propose the simulated annealing Genetic algorithm. The experimental result shows, can their is greater improvement on performance in this algorithm.
Key concepts: Simulated annealing, Adaptive simulated annealing, Genetic algorithm, Algorithm, Computer science, Random search, Population-based incremental learning, Mathematical optimization