Parallel Evolutionary Algorithm with Interleaving Generations

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F17%3A00477041" target="_blank" >RIV/67985807:_____/17:00477041 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11320/17:10361034

Výsledek na webu

<a href="http://dx.doi.org/10.1145/3071178.3071309" target="_blank" >http://dx.doi.org/10.1145/3071178.3071309</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3071178.3071309" target="_blank" >10.1145/3071178.3071309</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Parallel Evolutionary Algorithm with Interleaving Generations

Popis výsledku v původním jazyce

We present a parallel evolutionary algorithm with interleaving generations. The algorithm uses a careful analysis of genetic operators and selection in order to evaluate individuals from following generations while the current generation is still not completely evaluated. This brings significant advantages in cases where each fitness evaluation takes different amount of time, the evaluations are performed in parallel, and a traditional generational evolutionary algorithm has to wait for all evaluations to finish. The proposed algorithm provides better utilization of computational resources in these cases. Moreover, the algorithm is functionally equivalent to the generational evolutionary algorithm, and thus it does not have any evaluation time bias, which is often present in asynchronous evolutionary algorithms. The proposed algorithm is tested in a series of simple experiments and its effectiveness is compared to the effectiveness of the generational evolutionary algorithm in terms of CPU utilization.

Název v anglickém jazyce

Parallel Evolutionary Algorithm with Interleaving Generations

Popis výsledku anglicky

We present a parallel evolutionary algorithm with interleaving generations. The algorithm uses a careful analysis of genetic operators and selection in order to evaluate individuals from following generations while the current generation is still not completely evaluated. This brings significant advantages in cases where each fitness evaluation takes different amount of time, the evaluations are performed in parallel, and a traditional generational evolutionary algorithm has to wait for all evaluations to finish. The proposed algorithm provides better utilization of computational resources in these cases. Moreover, the algorithm is functionally equivalent to the generational evolutionary algorithm, and thus it does not have any evaluation time bias, which is often present in asynchronous evolutionary algorithms. The proposed algorithm is tested in a series of simple experiments and its effectiveness is compared to the effectiveness of the generational evolutionary algorithm in terms of CPU utilization.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA15-19877S" target="_blank" >GA15-19877S: Automatické modelování znalostí a plánů pro autonomní roboty</a><br>

Návaznosti

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

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

GECCO 2017. Proceedings of the 2017 Genetic and Evolutionary Computation Conference

ISBN

978-1-4503-4920-8

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

865-872

Název nakladatele

ACM

Místo vydání

New York

Místo konání akce

Berlin

Datum konání akce

15. 7. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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