Genetic Algorithm Based-On the Quantum Probability Representation

Cheng Yao Shi, Zhao Cheng Xuan, Chao Yang +1 more

MA Shu-xia, Weidong Jin

In this paper, a novel Quantum Genetic Algorithm (QGA) called Parallel Probability-gate Quantum Genetic Algorithm (PPQGA) is proposed.The main points of PPQGA are that a new chromosome representation called qubit representation, a new updating gate called probability-gate and a novel evolutionary strategy being used in update procedure are introduced.Based on the concepts and principles of quantum computing introduced, PPQGA is characterized by rapid convergence, good global search capability and the ability of possessing exploitation simultaneously.The experimental results of several special functions show that PPQGA can speedup the migration of the top individuals of subpopulations and is superior to other several genetic algorithms greatly in quality and efficiency.

Yang Yu, Hongwei Dai, Hui Li

Pawan Kumar Tiwari, Deo Prakash Vidyarthi

Noriyuki Muramoto, Nobuyuki Matsui, Teijiro Isokawa

Qubit-inspired Genetic Algorithm (QGA) is an extension of genetic algorithm in which quantum mechanics and its representations are introduced. A chromosome in our QGA is concretized as a series of quantum-bit (qubit) described by its complex-valued representation, and phase-rotation gates are embedded into the selection process over generations. In our previous work, it has been shown that this scheme has better performances than the classical ones in several problems such as N-K landscape problem, Knapsack Problem, Maximum Search, and Construction of image filters. In this paper, we make clear the effectiveness of the QGA by comparing QGA with GA through minimum solution problem on De Jong's functions.

Gexiang Zhang, Na Li, Weidong Jin +1 more