Performance Analysis of Quantum-Inspired Evolutionary Algorithm

摘要

Quantum-Inspired Evolutionary Algorithm (QEA) is an extension of evolutionary algorithm in which quantum mechanics and its representations are introduced. A chromosome in QEA is represented as a series of qubits (quantum bits), and phase-rotation gates are embedded into the selection process over generations. This algorithm has been shown to have better performances than the classical ones in small benchmark problems, but this has not yet been applied to larger scale problems. We show the performances of this QEA by solving the Knapsack problem, maximum search problem, and construction of image filter. We also investigate the diversity of individuals in a population in order to estimate the robustness against environmental changes.