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ANN-based classifiers automatically generated by new multi-objective bionic algorithm

机译:基于ANN的分类器由新的多目标仿生算法自动生成

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An artificial neural network (ANN) based classifier design using the modification of a meta-heuristic called Co-Operation of Biology Related Algorithms (COBRA) for solving multi-objective unconstrained problems with binary variables is presented. This modification is used for the ANN structure selection. The weight coefficients of the ANN are adjusted with the original version of COBRA. Two medical diagnostic problems, namely Breast Cancer Wisconsin and Pima Indian Diabetes, were solved with this technique. Experiments showed that both variants of COBRA demonstrate high performance and reliability in spite of the complexity of the optimization problems solved. ANN-based classifiers developed in this way outperform many alternative methods on the mentioned classification problems. The workability of the proposed meta-heuristic optimization algorithms was confirmed.
机译:呈现了一种基于基于神经网络(ANN)的分类器设计,所述分类器设计使用用于解决与二进制变量的多目标无约会问题的生物相关算法(COBRA)的元启发式被称为生物相关算法(COBRA)的荟萃启发式的荟萃设计。该修改用于ANN结构选择。 ANN的重量系数用上眼镜蛇的原始版本调整。用这种技术解决了两种医学诊断问题,即乳腺癌威斯康星州和PIMA印度糖尿病。实验表明,COBRA的两种变体都表现出高性能和可靠性,尽管解决了优化问题的复杂性。基于安基的分类器以这种方式开发出了许多关于提到的分类问题的替代方法。确认了所提出的荟萃启发式优化算法的可加工性。

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