Probability evolutionary algorithm for functional and combinatorial optimization
Shuhan Shen, Yuncai Liu · 2008
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).