Quantum novel genetic algorithm based on parallel subpopulation computing and its application View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2014-03

AUTHORS

Rigui Zhou, Jian Cao

ABSTRACT

A quantum novel genetic algorithm based on subpopulation parallel computing is presented, where quantum coding and rotation angle are improved to inspire more efficient genetic computing methods. In the algorithm, each axis of the solution space is divided into k parts, the individual (or chromosome) from each different subspace being coded differently, and the probability amplitude of each quantum bit or Q-bit is regarded as two paratactic genes. The basic quantum computing theory and classical quantum genetic algorithm are briefly introduced before a novel algorithm is presented for the function optimum and PID problem. Through a comparison between the novel algorithm and the classical counterpart, it is shown that the quantum inspired genetic algorithm performs better on running speed and optimization capability. More... »

PAGES

359-371

Journal

TITLE

Artificial Intelligence Review

ISSUE

3

VOLUME

41

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10462-012-9312-8

DOI

http://dx.doi.org/10.1007/s10462-012-9312-8

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1026188702


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