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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.

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.

'''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。

'''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。

=== 训练模型 Training models ===

=== 训练模型 Training models ===