An evolutionary machine learning: An adaptability perspective at fine granularity

Abstract

In what follows, we propose a new perspective of machine learning into genetic algorithms. The conceptualization of such G-reasoning relies on the semantic of adaptability to tackle efficiently large range of optimization problems. This paper intends to outperform genetic learning according to aβnearest-neighbors selection and a micro-learning schedule. Based upon an adaptation function, the learning behavior put emphasizes on adjustments of mutation rates through generations. Thus, to realize such way, two learning strategies are suggested. Commonly, the aim of this purpose is to regulate the intensity of convergence velocity along of evolution. Indeed, all mentioned requirements influence closely the performance of the algorithm. In addition to the best performance reached, comparisons are done with others evolutionary methods.