Adaptive population techniques in Evolution Algorithms

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24210%2F19%3A00006778" target="_blank" >RIV/46747885:24210/19:00006778 - isvavai.cz</a>

Výsledek na webu

<a href="http://mme2019.ef.jcu.cz/files/conference_proceedings.pdf" target="_blank" >http://mme2019.ef.jcu.cz/files/conference_proceedings.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Adaptive population techniques in Evolution Algorithms

Popis výsledku v původním jazyce

Evolution algorithms are one of the most popular optimization techniques for its ability to solve wide field of general combinatorial problems. They are simple to implement and use as its mechanisms are logical and untestable due to its natural and common sense. However its optimization results as well as timespan are heavy dependent on setting searching parameters as crossover, mutation rates, population size etc. Setting those parameters requires intuition and experience and is also beneficial to change them during optimization as necessity to exploit and explore changes during optimization. Those are the reasons why nowadays research in the field of Evolutionary Computing is focusing more and more on the adaptive operator control. This article is focusing on part of adaptive parameter control which is known not only as responsible for quality of results but also for optimization time. The research is focusing on population sizing schemes together with selection and elimination procedures. It is reviewing known techniques and presenting original solution. Those techniques are then tested on known theoretical job shop scheduling problems and their efficiency is discussed. This paper was written with aid of students (Vignesh Babu Kuduva Gopinath and Jiří Bareš).

Název v anglickém jazyce

Adaptive population techniques in Evolution Algorithms

Popis výsledku anglicky

Evolution algorithms are one of the most popular optimization techniques for its ability to solve wide field of general combinatorial problems. They are simple to implement and use as its mechanisms are logical and untestable due to its natural and common sense. However its optimization results as well as timespan are heavy dependent on setting searching parameters as crossover, mutation rates, population size etc. Setting those parameters requires intuition and experience and is also beneficial to change them during optimization as necessity to exploit and explore changes during optimization. Those are the reasons why nowadays research in the field of Evolutionary Computing is focusing more and more on the adaptive operator control. This article is focusing on part of adaptive parameter control which is known not only as responsible for quality of results but also for optimization time. The research is focusing on population sizing schemes together with selection and elimination procedures. It is reviewing known techniques and presenting original solution. Those techniques are then tested on known theoretical job shop scheduling problems and their efficiency is discussed. This paper was written with aid of students (Vignesh Babu Kuduva Gopinath and Jiří Bareš).

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

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

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název statě ve sborníku

37th International Conference on Mathematical Methods in Economics 2019

ISBN

978-80-7394-760-6

ISSN

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e-ISSN

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Počet stran výsledku

6

Strana od-do

67-72

Název nakladatele

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Místo vydání

České Budějovice

Místo konání akce

České Budějovice

Datum konání akce

1. 1. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000507570400010