Genetic algorithm (GA) is a nature-inspired algorithm to produce best possible solution by selecting the fittest individual from a pool of possible solutions. Like most of the optimization techniques, the GA can also stuck in the local optima, producing a suboptimal solution. This work presents a novel metaheuristic optimizer named as the binary chaotic genetic algorithm (BCGA) to improve the GA performance. The chaotic maps are applied to the initial population, and the reproduction operations follow. To demonstrate its utility, the proposed BCGA is applied to a feature selection task from an affective database, namely AMIGOS (A Dataset for Affect, Personality and Mood Research on Individuals and Groups) and two healthcare datasets having large feature space. Performance of the BCGA is compared with the traditional GA and two state-of-the-art feature selection methods. The comparison is made based on classification accuracy and the number of selected features. Experimental results suggest promising capability of BCGA to find the optimal subset of features that achieves better fitness values. The obtained results also suggest that the chaotic maps, especially sinusoidal chaotic map, perform better as compared to other maps in enhancing the performance of raw GA. The proposed approach obtains, on average, a fitness value twice as better than the one achieved through the raw GA in the identification of the seven classes of emotions.

遗传算法（GA）是一种自然启发式算法，可通过从可能的解决方案库中选择最适合的个体来产生最佳的解决方案。与大多数优化技术一样，遗传算法也可能陷入局部最优状态，从而产生次优的解决方案。这项工作提出了一种新颖的元启发式优化器，称为二进制混沌遗传算法（BCGA），以提高GA性能。将混沌图应用于初始种群，然后进行复制操作。为了证明其实用性，将提出的BCGA应用于情感数据库的特征选择任务，即AMIGOS（个人和群体的情感，个性和情绪研究数据集）和两个具有较大特征空间的医疗数据集。将BCGA的性能与传统的GA和两种最新的特征选择方法进行了比较。根据分类精度和所选特征的数量进行比较。实验结果表明，BCGA能够找到具有更好适应性的最佳特征子集。所获得的结果还表明，与其他图相比，混沌图，尤其是正弦形混沌图在增强原始GA性能方面表现更好。在识别七类情绪时，所提出的方法平均获得的适应性值是通过原始遗传算法获得的适应性值的两倍。实验结果表明，BCGA能够找到具有更好适应性的最佳特征子集。所获得的结果还表明，与其他图相比，混沌图，尤其是正弦形混沌图在增强原始GA性能方面表现更好。在识别七类情绪时，所提出的方法平均获得的适应性值是通过原始遗传算法获得的适应性值的两倍。实验结果表明，BCGA能够找到具有更好适应性的最佳特征子集。所获得的结果还表明，与其他图相比，混沌图，尤其是正弦形混沌图在增强原始GA性能方面表现更好。在识别七类情绪时，所提出的方法平均获得的适应性值是通过原始遗传算法获得的适应性值的两倍。