AGAVaPS - Adaptive Genetic Algorithm with Varying Population Size

Recently there is great interest in optimization, especially on meta-heuristic algorithms. Many works have proposed improvements for these algorithms for general and specific applications. In this paper the Adaptive Genetic Algorithm with Varying Population Size (AGAVaPS) is proposed, an improvement of Genetic Algorithm. On the AGAVaPS each solution has their own mutation rate and number of iterations that the solution will be in the population. The proposed optimizer is tested against six other well established optimizers on the CEC2017 single objective optimization benchmark functions considering coverage of the search space and quality of solution obtained. It is also tested for feature selection and Bayesian network structural learning. The evolution of the population size over the iterations is also analysed. The results obtained show that the AGAVaPS has a very competitive performance in both, coverage and quality of solution.