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| | ==== 遗传算法 Genetic algorithms ==== | | ==== 遗传算法 Genetic algorithms ==== |
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| − | {{Main|Genetic algorithm}}
| + | :''主文章：[https://en.wikipedia.org/wiki/Genetic_algorithm 遗传算法]'' |
| − | | + | '''遗传算法 Genetic Algorithm，GA'''是一种模拟[https://en.wikipedia.org/wiki/Natural_selection 自然选择]过程的[https://en.wikipedia.org/wiki/Search_algorithm 搜索][https://en.wikipedia.org/wiki/Heuristic_(computer_science) 启发]式算法，它利用[https://en.wikipedia.org/wiki/Mutation_(genetic_algorithm) 变异]和[https://en.wikipedia.org/wiki/Crossover_(genetic_algorithm) 重组]等方法生成新的[https://en.wikipedia.org/wiki/Chromosome_(genetic_algorithm) 基因型]，以期为给定问题找到好的解决方案。在机器学习中，遗传算法在20世纪80年代和90年代得到了一些应用 |
| − | A genetic algorithm (GA) is a [[search algorithm]] and [[heuristic (computer science)|heuristic]] technique that mimics the process of [[natural selection]], using methods such as [[Mutation (genetic algorithm)|mutation]] and [[Crossover (genetic algorithm)|crossover]] to generate new [[Chromosome (genetic algorithm)|genotype]]s in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s.<ref>{{cite journal |last1=Goldberg |first1=David E. |first2=John H. |last2=Holland |title=Genetic algorithms and machine learning |journal=[[Machine Learning (journal)|Machine Learning]] |volume=3 |issue=2 |year=1988 |pages=95–99 |doi=10.1007/bf00113892|url=https://deepblue.lib.umich.edu/bitstream/2027.42/46947/1/10994_2005_Article_422926.pdf }}</ref><ref>{{Cite journal |title=Machine Learning, Neural and Statistical Classification |journal=Ellis Horwood Series in Artificial Intelligence |first1=D. |last1=Michie |first2=D. J. |last2=Spiegelhalter |first3=C. C. |last3=Taylor |year=1994 |bibcode=1994mlns.book.....M }}</ref> Conversely, machine learning techniques have been used to improve the performance of genetic and [[evolutionary algorithm]]s.<ref>{{cite journal |last1=Zhang |first1=Jun |last2=Zhan |first2=Zhi-hui |last3=Lin |first3=Ying |last4=Chen |first4=Ni |last5=Gong |first5=Yue-jiao |last6=Zhong |first6=Jing-hui |last7=Chung |first7=Henry S.H. |last8=Li |first8=Yun |last9=Shi |first9=Yu-hui |title=Evolutionary Computation Meets Machine Learning: A Survey |journal=Computational Intelligence Magazine |year=2011 |volume=6 |issue=4 |pages=68–75 |doi=10.1109/mci.2011.942584}}</ref>
| + | <ref>{{cite journal |last1=Goldberg |first1=David E. |first2=John H. |last2=Holland |title=Genetic algorithms and machine learning |journal=Machine Learning |volume=3 |issue=2 |year=1988 |pages=95–99 }}</ref><ref>{{cite book |title=Machine Learning, Neural and Statistical Classification |first1=D. |last1=Michie |first2=D. J. |last2=Spiegelhalter |first3=C. C. |last3=Taylor |year=1994 |publisher=Ellis Horwood}}</ref>。 |
| − | | + | 相反，机器学习技术被用来改进遗传算法和[https://en.wikipedia.org/wiki/Evolutionary_algorithm 进化算法]的性能 |
| − | A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s. Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.
| + | <ref>{{cite journal |last1=Zhang |first1=Jun |last2=Zhan |first2=Zhi-hui |last3=Lin |first3=Ying |last4=Chen |first4=Ni |last5=Gong |first5=Yue-jiao |last6=Zhong |first6=Jing-hui |last7=Chung |first7=Henry S.H. |last8=Li |first8=Yun |last9=Shi |first9=Yu-hui |title=Evolutionary Computation Meets Machine Learning: A Survey |journal=Computational Intelligence Magazine |year=2011 |volume=6 |issue=4 |pages=68–75 |url=http://ieeexplore.ieee.org/iel5/10207/6052357/06052374.pdf?arnumber=6052374 }}</ref>。 |
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| − | '''遗传算法 Genetic Algorithm，GA'''是一种模仿自然选择过程的搜索算法和启发式技术，利用变异和交叉等方法产生新的基因型，以期为给定的问题找到最优解。在机器学习中，遗传算法在20世纪80年代和90年代被广泛使用，而现在的机器学习技术已经可以被用来改善遗传和进化算法的性能。
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| | === 训练模型 Training models === | | === 训练模型 Training models === |