Metaheuristic optimization with the Differential Evolution algorithm

Learn the theory of the Differential Evolution algorithm, its Python implementation and how and why it will surely help you in solving complex real-world optimization problems. This article has been written with Salvatore Guastella. Optimization is a pillar of data science. If you think about it, under the hood of each machine learning algorithms (ranging from basic linear regression to the most complex neural networks architectures), an optimization problem is solved. Moreover, in many real-world problems the goal is to find the values of one or more decision variables that minimize (or maximize) a quantity of interest while satisfying certain constraints. Few examples are given by portfolio optimization in finance, profit maximization of ad campaigns, energy efficiency in energy plants and shipment cost minimization in logistics (refer to this Medium article [1] in our Eni digiTALKS channel for an interesting example).