Applied Mathematical Sciences, Vol. 17, 2023, no. 1, 15 - 25 HIKARI Ltd, www.m-hikari.com https://doi.org/10.12988/ams.2023.917307 Genetic Algorithm for Optimal Multivariate Mixture Giacinto Angelo Sgarro University of Foggia Department of Economics, Management and Territory, Italy Corresponding author Luca Grilli University of Foggia Department of Economics, Management and Territory, Italy This article is distributed under the Creative Commons by-nc-nd Attribution License. Copyright c  2023 Hikari Ltd. Abstract This paper proposes an algorithm to find an optimal mixture that is as close as possible to an ideal solution, starting from a set of elements (items) described by a set of variables (features). This class of optimiza- tion problems can be tackled through traditional approaches belonging to the field of operations research (OR) or even through meta-heuristics techniques belonging to the field of artificial intelligence (AI). In order to present an artificial intelligence perspective, this paper uses a ge- netic algorithm (GA) model which proves its consistency through the comparison with a linear programming (LP) solver on a set of 8-items 5-features experiments. Results show that the proposed GA converges towards the global optimum and provides competitive results. Mathematics Subject Classification: 68W50, 68U35, 90C29, 62H86 Keywords: Genetic algorithms; Optimization; Multivariate; Artificial in- telligence