A Novel Genetic Algorithm Based on Immunity and Its Application

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F12%3A86084777" target="_blank" >RIV/61989100:27740/12:86084777 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/HIS.2012.6421396" target="_blank" >http://dx.doi.org/10.1109/HIS.2012.6421396</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/HIS.2012.6421396" target="_blank" >10.1109/HIS.2012.6421396</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A Novel Genetic Algorithm Based on Immunity and Its Application

Popis výsledku v původním jazyce

In this paper, a novel genetic algorithm based on immunity (GABI) on the basis of parallel genetic algorithms (PGA) is proposed in order to overcome some defects of them, such as premature and slow convergence rate. The global performance of the algorithm is improved by introducing immunity theory into PGA. This is revealed in the following two aspects. One is that the immune selection based on proposed adjustable geometric-progression rank-based selection can prevent the algorithm from premature. The other is that convergence rate can be accelerate by individual migration strategy between subpopulations based on immune memory mechanism. In this algorithm, the idea of multiple subpopulations evolution based on improved adaptive crossover and mutation is adopted. To be hybridized with the Powell method can further improve local searching performance of the algorithm. An example of layout design shows that GABI is feasible and effective. 2012 IEEE.

Název v anglickém jazyce

A Novel Genetic Algorithm Based on Immunity and Its Application

Popis výsledku anglicky

In this paper, a novel genetic algorithm based on immunity (GABI) on the basis of parallel genetic algorithms (PGA) is proposed in order to overcome some defects of them, such as premature and slow convergence rate. The global performance of the algorithm is improved by introducing immunity theory into PGA. This is revealed in the following two aspects. One is that the immune selection based on proposed adjustable geometric-progression rank-based selection can prevent the algorithm from premature. The other is that convergence rate can be accelerate by individual migration strategy between subpopulations based on immune memory mechanism. In this algorithm, the idea of multiple subpopulations evolution based on improved adaptive crossover and mutation is adopted. To be hybridized with the Powell method can further improve local searching performance of the algorithm. An example of layout design shows that GABI is feasible and effective. 2012 IEEE.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/ED1.1.00%2F02.0070" target="_blank" >ED1.1.00/02.0070: Centrum excelence IT4Innovations</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2012

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 2012 12th International Conference on Hybrid Intelligent Systems, HIS 2012

ISBN

978-1-4673-5115-7

ISSN

—

e-ISSN

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Počet stran výsledku

6

Strana od-do

566-571

Název nakladatele

IEEE

Místo vydání

New York

Místo konání akce

Pune

Datum konání akce

4. 12. 2012

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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