Quantum behaved genetic algorithm: Constraints-handling and GPU computing

Abstract

Quantum-inspired evolutionary algorithm is a new evolutionary algorithm using concepts and principles of quantum computing to work on classical computer rather than quantum mechanical hardware. This article introduces main concepts behind the intersection between evolutionary algorithms and quantum computing, such as quantum-bit, superposition feature, quantum gate, quantum measurement and quantum interference. These behaviors of quantum concepts offer computational power and computational intelligence that must be harnessed and used. Intelligence is the main focus to design novel constraint-handling technique with quantum behaved genetic algorithm (QBGA) to solve well known constrained benchmark problems. Single quantum chromosome represents multiple solutions at the same time, so the same infeasible solutions based on quantum features are also feasible ones. Finally GPU (Graphics Processing Unit) will be discussed with (QBGA) to achieve parallel processing and speed up execution time, especially to solve high dimensional real world optimization problems requiring intensive computing resources.