2003Unpublished venueRequires access

Performance Appraisement of the Simulated Annealing Genetic Algorithms

FU Yong-feng

Open publisher page 1 citations

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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What this paper is about

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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Available 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.

Key concepts: Simulated annealing, Adaptive simulated annealing, Genetic algorithm, Algorithm, Computer science, Random search, Population-based incremental learning, Mathematical optimization

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