  This paper proposes a generalized Hybrid Real-coded Quantum Evolutionary
Algorithm (HRCQEA) for optimizing complex functions as well as combinatorial
optimization. The main idea of HRCQEA is to devise a new technique for mutation
and crossover operators. Using the evolutionary equation of PSO a
Single-Multiple gene Mutation (SMM) is designed and the concept of Arithmetic
Crossover (AC) is used in the new Crossover operator. In HRCQEA, each triploid
chromosome represents a particle and the position of the particle is updated
using SMM and Quantum Rotation Gate (QRG), which can make the balance between
exploration and exploitation. Crossover is employed to expand the search space,
Hill Climbing Selection (HCS) and elitism help to accelerate the convergence
speed. Simulation results on Knapsack Problem and five benchmark complex
functions with high dimension show that HRCQEA performs better in terms of
ability to discover the global optimum and convergence speed.
