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Feature selection based on hybridization of genetic algorithm and competitive swarm optimizer

机译：基于遗传算法杂交和竞争性群优化器的特征选择

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Feature selection is one of the hottest machine learning topics in recent years. The main purposes of it are to simplify the original model, improve the readability of the model, and prevent over-fitting by searching for a suitable subset of features. There are many methods for this problem, including evolutionary algorithms and particle swarm optimization. Among them, the competitive swarm optimizer is a new optimization algorithm proposed in recent years, which is based on particle swarm optimization algorithm, and has achieved good results in high-dimensional feature selection problems, but it also has the problems of high computation time cost and easily being premature. Aiming at these problems, this paper proposes to add the crossover operator and mutation operator in the genetic algorithm to the competitive swarm optimization, so as to improve the generation speed of new individuals in the algorithm and prevent premature population. After testing on UC Irvine Machine Learning Repository, the new algorithm not only improves the computational efficiency, but also avoids the problem that the competitive swarm optimization algorithm is easy to fall into the local optimum, which greatly improves the calculation effect.
机译：特色选择是近年来最热门的机器学习主题之一。它的主要目的是简化原始模型，提高模型的可读性，并通过搜索合适的特征子集来防止过度拟合。该问题有许多方法，包括进化算法和粒子群优化。其中，竞争性群优化器是近年来提出的新优化算法，该算法基于粒子群优化算法，并且在高维特征选择问题中取得了良好的结果，但它也具有高计算时间成本的问题并且容易过早。针对这些问题，本文提出以遗传算法在竞争群体优化中添加交叉运算符和突变运算符，从而提高算法中新人的发电速度，防止过早群体。在UC IRVINE机器学习存储库上进行测试后，新算法不仅提高了计算效率，还可以避免竞争群优化算法容易落入本地最佳的问题，这大大提高了计算效果。

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