Evolutionary Algorithms for Quantum Computers

Anthony Brabazon, Michael O’Neill, Seán McGarraghy

Shuhan Shen, Yuncai Liu

A novel evolutionary algorithm called Probability Evolutionary Algorithm (PEA) is proposed, which is inspired by the Quantum computation and Quantum-inspired Evolutionary Algorithm (QEA). The individual in PEA is encoded by a probabilistic superposed bit which can represent a linear superposition of the states 0 to k (k ≥ 1). The observing step is used in PEA to obtain the observed individual, and the update method is used to evolve the population. The function optimization and 0-k knapsack problem experiments show that PEA has apparent superior in application area, searching capability and computation time compared with QEA and canonical genetic algorithm (CGA).

Gexiang Zhang

Xie Jin-hua

Quantum Evolutionary Algorithm(QEA) is a distinctive type of algorithm for optimization currently,and the theoretical basis of QEA is quantum computation.The algorithm takes advantage of intervention and parallelism of quantum bit thoroughly,which enables QEA to solve combinatorial optimization problems.While solving scheduling problems,QEA has defects that it converges slowly and doesn’t use other immature individual.Hybrid Quantum Algorithm(HQA) is formed and it sucks Particle Swarm Optimization algorithm(PSO) and evolutionary computation into QEA.Classical flow shop problem is employed to test the algorithm,and the result shows that the hybrid algorithm overcomes the defects of QEA and it has universality to solve scheduling problems.

Chang Wook Ahn, Jun Suk Kim

Giovanni Acampora, Autilia Vitiello