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首页> 外文会议>Mexican International Conference on Artificial Intelligence >Solving Dynamic Combinatorial Optimization Problems Using a Probabilistic Distribution as Self-adaptive Mechanism in a Genetic Algorithm
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Solving Dynamic Combinatorial Optimization Problems Using a Probabilistic Distribution as Self-adaptive Mechanism in a Genetic Algorithm

机译：用概率分布作为遗传算法中自适应机制的概率分布解决动态组合优化问题

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摘要

In recent years, the interest to solve dynamic combinatorial optimization problems has increased. Metaheuristic algorithms have been used to find good solutions in a reasonably low time, in addition, the use of self-adaptive strategies has increased considerably because they have proved to be a good option to improve performance in these algorithms. In this research, a self-adaptive mechanism is developed to improve the performance of the genetic algorithm for dynamic combinatorial problems, using the strategy of genotype-phenotype mapping and probabilistic distributions. Results demonstrate the capability of the mechanism to help algorithms to adapt in dynamic environments.
机译：近年来，解决动态组合优化问题的兴趣增加。成群质算法已被用来在合理低的时间内找到良好的解决方案，此外，使用自适应策略的使用增加了很大，因为它们已被证明是提高这些算法中性能的良好选择。在本研究中，利用基因型 - 表型映射和概率分布的策略来开发自适应机制以提高动态组合问题的遗传算法的性能。结果展示了该机制帮助算法适应动态环境的能力。

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