This paper proposes a Hybrid Quantum Evolutionary Algorithm. Quantum computing is capable of processing huge numbers of quantum states simultaneously, in parallel, (“quantum parallelism”), whereas evolutionary computing can only process one chromosome per processor. In theory, QC ought to be able to process upto all possible points in a 2 search space (of N bit chromosomes). An evolutionary alg...

Abstract Variable-length encoding evolutionary algorithm has been proved effective in small and medium-sized reversible logic synthesis. chromosome is adopted because the length of optimal circuit unknown. However, it leads to huge search space. The improved two-stage hybrid synthesis proposed reduce space enhance feasible ratio. Fixed variable-length are applied two stages respectively. Estima...

The quantum evolutionary algorithm is based on the quantum superposition state and the quantum computing. Compared to the traditional evolutionary algorithm, QEA has great global search ability; however, its complexity limits the operational ability and the efficiency, at the same time, its local search ability is relatively poor. In this paper, the author presents an improved hybrid intelligen...

Large set of linear equations, especially for sparse and structured coefficient (matrix) equations, solutions using classical methods become arduous. And evolutionary algorithms have mostly been used to solve various optimization and learning problems. Recently, hybridization of classical methods (Jacobi method and Gauss-Seidel method) with evolutionary computation techniques have successfully ...

algorithm Iztok Fister,∗ Marjan Mernik,† and Bogdan Filipič‡ Abstract This paper proposes a hybrid self-adaptive evolutionary algorithm for graph coloring that is hybridized with the following novel elements: heuristic genotype-phenotype mapping, a swap local search heuristic, and a neutral survivor selection operator. This algorithm was compared with the evolutionary algorithm with the SAW met...

Evolutionary computation has become an important problem solving methodology among many researchers. The population-based collective learning process, selfadaptation, and robustness are some of the key features of evolutionary algorithms when compared to other global optimization techniques. Even though evolutionary computation has been widely accepted for solving several important practical ap...