An Efficient Quantum Inspired Genetic Algorithm with Chaotic Map Model Based Interference and Fuzzy Objective Function for Gray Level Image Thresholding

Abstract

A genetic algorithm inspired by the inherent features of parallelism and time discreteness exhibited by quantum mechanical systems, is presented in this article. The predominant interference operator in the proposed quantum inspired genetic algorithm (QIGA) is influenced by time averages of different random chaotic map models derived from the randomness of quantum mechanical systems. Subsequently, QIGA uses quantum inspired crossover and mutation on the trial solutions, followed by a quantum measurement on the intermediate states, to derive sought results. Application of QIGA to determine optimum threshold intensities is demonstrated on two real life gray level images. The efficacy of QIGA is adjudged w.r.t. a convex combination of two fuzzy thresholding evaluation metrics in a multiple criterion scenario. Comparative study of its performance with the classical counterpart indicates encouraging avenues.