Title :
Probability evolutionary algorithm for functional and combinatorial optimization
Author :
Shen, Shuhan ; Liu, Yuncai
Author_Institution :
Inst. of Image Process. & Pattern Recognition, Shanghai Jiao Tong Univ., Shanghai
Abstract :
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 ges 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).
Keywords :
combinatorial mathematics; genetic algorithms; knapsack problems; probability; quantum computing; canonical genetic algorithm; combinatorial optimization; functional optimization; knapsack problem; linear superposition; probabilistic superposed bit; probability evolutionary algorithm; quantum computation; quantum-inspired evolutionary algorithm; Automation; Biology computing; Evolution (biology); Evolutionary computation; Genetic algorithms; Image coding; Image processing; Intelligent control; Pattern recognition; Quantum computing; 0-k knapsack problem; evolutionary algorithm; function optimization; probability evolutionary algorithm;
Conference_Titel :
Intelligent Control and Automation, 2008. WCICA 2008. 7th World Congress on
Conference_Location :
Chongqing
Print_ISBN :
978-1-4244-2113-8
Electronic_ISBN :
978-1-4244-2114-5
DOI :
10.1109/WCICA.2008.4594592
