Challenges in evolutionary algorithm to find optimal parameters of SVM: A review

Abstract In rapidly changing classification and predation environment, optimization techniques in determining the hyper parameter of Support Vector Machine has become crucial for the accuracy of result. It’s an important tool for improving output quality of classification and prediction which includes modeling parameters relationship and resolution of optimal hyper parameter. However, determination of regularization (C) and gamma (γ), through mathematical models have undergone substantial development and expansion. In this paper, optimization techniques are categorized under several criteria. We also included the benchmarks for measuring the performance of classifier after parameter tuning, and found there is still a scope of improvement.