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.
Their experiment results showed that both variants of COBRA demonstrate high performance and reliability in spite of the complexity of the optimization problems solved REF .
ANN-based classifiers automatically generated by new multi-objective bionic algorithm
2015 12th International Conference on Informatics in Control, Automation and Robotics (ICINCO)
2015 12th International Conference on Informatics in Control, Automation and Robotics (ICINCO)
Computer Science