Expediting Population Diversification in Evolutionary Computation with Quantum Algorithm

Abstract

Attempts to introduce quantum application to various fields in computer science are growing in numbers as days of commercialized, fully functional quantum computers come closer. Quantum computing's uniqueness in commencing parallel computation renders unprecedentedly efficient optimization possible. This paper introduces the adaptation of quantum processing to Crowding, one of the genetic algorithmic procedures to secure undeveloped individual chromosomes in pursuit of diversifying the target population. We argue that the nature of genetic algorithm to find the best solution in the process of optimization can be greatly enhanced by the capability of quantum computing to perform multiple computations in parallel. By introducing the relevant quantum mathematics based on Grover's Selection Algorithm and constructing its mechanism in a quantum simulator, we come to conclusion that our proposed approach is valid in such a way that it can precisely reduce the amount of computation query to finish the crowding process without any impairment in the middle of genetic operations.