This paper analyzes the principle and characteristics of genetic algorithm and introduces an improved algorithm combining with simulated annealing algorithm ...
Genetic Algorithms. Genetic algorithms attempt to find optimal solutions by using concepts related to the process of natural selection: inheritance mutation.
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This paper considers the problem of scheduling jobs on machines in an open shop environment so that the sum of completion times or mean flow time becomes ...
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Nov 4, 2010 · A Genetic Algorithm maintains a population of possible solutions, and at each step, selects pairs of possible solution, combines them (crossover) ...
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Dec 12, 2018 · Simulated annealing algorithms are generally better at solving mazes, because they are less likely to get suck in a local minima because of their probabilistic ...
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Jun 1, 2016 · Simulated annealing algorithm can avoid premature phenomena, but there may be slow convergence. After the combination of the two algorithms, the ...
Nov 6, 2018 · In this paper, we propose a genetic simulated annealing (GSA) algorithm to improve the efficiency of transforming other kinds of networks into small-world ...
Dec 10, 2018 · SA is a single solution based algorithm, while GA is a population based algorithm. Meaning that, SA starts with only one solution and try to enhance it, while ...
Mar 28, 2024 · This study presents a comprehensive approach for optimizing the acquisition, utilization, and maintenance of ABLVR vascular robots in healthcare settings.
Genetic algorithms and simulated annealing are leading methods of search and optimization. This paper proposes an efficient hybrid algorithm named ASAGA ...
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