Caller interaction classification: A comparison of real and binary coded GA-MLP techniques

This paper employs pattern classification methods for assisting contact centers in determining caller interaction at a 'Say account' field within an Interactive Voice Response application. Binary and real coded genetic algorithms (GAs) that employed normalized geometric ranking as well as tournament selection functions were utilized to optimize the Multi-Layer Perceptron neural network architecture. The binary coded genetic algorithm (GA) that used tournament selection function yielded the most optimal solution. However, this algorithm was not the most computationally efficient. This algorithm demonstrated acceptable repeatability abilities. The binary coded GA that used normalized geometric selection function yielded poor repeatability capabilities. GAs that employed normalized geometric ranking selection function were computationally efficient, but yielded solutions that were approximately equal. The real coded tournament selection function GA produced classifiers that were approximately 3% less accurate than the binary coded tournament selection function GA.