Co-operation of Biology Related Algorithms meta-heuristic in ANN-based classifiers design

Meta-heuristic called Co-Operation of Biology Related Algorithms (COBRA), that has earlier demonstrated its usefulness on CEC´2013 real-valued optimization competition benchmark, is applied to ANN-based classifiers design. The basic idea consists in representation of ANN´s structure as a binary string and the use of the binary modification of COBRA for the ANN´s structure selection. Neural network´s weight coefficients represented as a string of real-valued variables are adjusted with the original version of COBRA. Four benchmark classification problems (two bank scoring problems and two medical diagnostic problems) are solved with this approach. Multilayered feed-forward ANNs with maximum 5 hidden layers and maximum 5 neurons on each layer are used. It means that ANN´s structure optimal selection requires solving an optimization problem with 100 binary variables. Fitness function calculation for each bit string requires solving an optimization problem with up to 225 real-valued variables. Experiments showed that both variants of COBRA demonstrate high performance and reliability in spite of the complexity of solved optimization problems. ANN-based classifiers developed in this way outperform many alternative methods on mentioned benchmark classification problems. The workability and usefulness of proposed meta-heuristic optimization algorithms are confirmed.