Multiwinner Voting in Genetic Algorithms

Abstract

Genetic algorithms are a group of powerful tools for solving ill-posed global optimization problems in continuous domains. When insensitivity in the fitness function is an obstacle, the most desired feature of a genetic algorithm is its ability to explore plateaus of the fitness function surrounding its minimizers. The authors suggest a way of maintaining diversity of the population in the plateau regions based on a new approach for selection according to the theory of multiwinner elections among autonomous agents. The article delivers a detailed description of the new selection algorithm, computational experiments that put the choice of the proper multiwinner rule to use, and a preliminary experiment showing the proposed algorithm's effectiveness in exploring a fitness function's plateau.