An efficient evolutionary algorithm to optimize the Choquet integral

Abstract

Information fusion is an essential part of nearly all systems whose goal is to derive decisions from multiple sources. Often, a fusion solution has parameters and the goal is to learn them from data. Herein, we propose efficient evolutionary algorithm (EA) operators to facilitate learning the Choquet integral (ChI). Whereas many EAs provide a way to solve complex, unconstrained optimization tasks, most tend to perform relatively poor in light of constraints. Recently, a few EA-based approaches to optimizing the ChI have appeared. Namely, these methods focus on fixing the values of variables so conditions are met or feasible candidate pairs are identified for steps such as crossover. Herein, we introduce a new set of transparent operators that are guaranteed to naturally preserve constraints, thus eliminating the need to resort to costly evaluations and fixing of constraint violations. In particular, our method scales well to large numbers of inequality constraints, something that prior work does not. The proposed algorithm, coined efficient ChI genetic algorithm (ECGA), is evaluated on several synthetic data sets and it is compared with state-of-the-art algorithms. In particular, we show benefits in terms of solutions found and the time it takes to find such an answer.