Journal of Heuristics

, Volume 17, Issue 3, pp 303–351

Quantum-inspired evolutionary algorithms: a survey and empirical study

Article

DOI: 10.1007/s10732-010-9136-0

Cite this article as:
Zhang, G. J Heuristics (2011) 17: 303. doi:10.1007/s10732-010-9136-0

Abstract

Quantum-inspired evolutionary algorithms, one of the three main research areas related to the complex interaction between quantum computing and evolutionary algorithms, are receiving renewed attention. A quantum-inspired evolutionary algorithm is a new evolutionary algorithm for a classical computer rather than for quantum mechanical hardware. This paper provides a unified framework and a comprehensive survey of recent work in this rapidly growing field. After introducing of the main concepts behind quantum-inspired evolutionary algorithms, we present the key ideas related to the multitude of quantum-inspired evolutionary algorithms, sketch the differences between them, survey theoretical developments and applications that range from combinatorial optimizations to numerical optimizations, and compare the advantages and limitations of these various methods. Finally, a small comparative study is conducted to evaluate the performances of different types of quantum-inspired evolutionary algorithms and conclusions are drawn about some of the most promising future research developments in this area.

Keywords

Quantum-inspired evolutionary algorithm Evolutionary computation Quantum computing Optimization 

Glossary

QIEA

Quantum-inspired evolutionary algorithm

EDQA

Evolutionary-designed quantum algorithm

Q-bit

Quantum-inspired bit

EA

Evolutionary algorithm

GA

Genetic algorithm

Q-gate

Quantum-inspired gate

QEA

Quantum evolutionary algorithm

bQIEAcm

bQIEA with crossover and mutation operators

bQIEAn

bQIEA with a novel update method for Q-gates

bQIEAi

Hybrid algorithm of bQIEA and immune algorithms

DS-CDMA

Directed-sequence code-division multiple access

bQIEApso

Hybrid algorithm of bQIEA and PSO

bQIEAcga

Hybrid algorithm of bQIEA and CGA

bQIEAo

Original version of bQIEA

bQIEAh

Hybrid bQIEA

rQIEA

Real observation QIEA

EDA

Estimation of distribution algorithm

bQIEA

Binary observation QIEA

OMUD

Optimal multiuser detector

PGA

Polyploid GA

PSO

Particle swarm optimization

MFD

Matched filter detector

CGA

Conventional GA

iQIEA

QIEA-like algorithm

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.School of Electrical EngineeringSouthwest Jiaotong UniversityChengduP.R. China

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