The analysis of histological images is based on visual assessment of tissues by specialists using an optical microscopy. This task can be time-consuming and challenging, mainly due to the complexity of the structures and diseases under investigation. These facts have motivated the development of computational methods to support specialists in research and decision-making. Despite the different computational strategies available in the literature, the solutions based on genetic algorithm have not been fully explored to provide the best combination of features, selection algorithms and classifiers. In this paper, we describe an approach based on genetic algorithm able to evaluate a significant number of features, selection methods and classifiers in order to provide an acceptable association for the diagnosis and pattern recognition of non-Hodgkin lymphomas and colorectal cancer. The chromosomal structure was represented with four genes. The evaluation and selection of individuals, as well as the crossover and mutation processes, were defined to distinguish the groups under investigation, with the highest AUC value and the smallest number of features. The tests were performed considering 1512 features from histological images, different population sizes and number of iterations. An initial population of 50 individuals and 50 iterations provided the best result (AUC value of 0.984) for the colorectal histological images. For non-Hodgkin lymphoma images, the best result (AUC value of 0.947) was obtained with a population of 500 individuals and 50 iterations. The proposed methodology with detailed information regarding the methods, features and best associations are relevant contributions for the community interested in the study of pattern recognition of colorectal cancer and lymphomas.

组织学图像的分析基于专家使用光学显微镜对组织的视觉评估。主要由于所研究的结构和疾病的复杂性，该任务可能是耗时且具有挑战性的。这些事实激励了计算方法的发展，以支持研究和决策专家。尽管文献中提供了不同的计算策略，但尚未充分探索基于遗传算法的解决方案以提供功能，选择算法和分类器的最佳组合。在本文中，我们描述了一种基于遗传算法的方法，该方法能够评估大量特征，选择方法和分类器，以便为非霍奇金淋巴瘤和大肠癌的诊断和模式识别提供可接受的关联。染色体结构由四个基因表示。定义了个体的评估和选择以及交叉和突变过程，以区分具有最高AUC值和最少数量特征的被调查群体。考虑组织学图像，不同的种群大小和迭代次数的1512个特征进行了测试。50个个体的初始种群和50次迭代为结直肠组织学图像提供了最佳结果（AUC值为0.984）。对于非霍奇金淋巴瘤图像，在500个人和50次迭代的情况下获得了最佳结果（AUC值为0.947）。