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Statements

Subject Item
n2:RIV%2F70883521%3A28140%2F07%3A63505862%21RIV08-MSM-28140___
rdf:type
skos:Concept n15:Vysledek
dcterms:description
Článek se zabývá novou metodou pro symbolickou regresi - Analytickým programováním (AP), který dokáže řešit různé problémy z oblasti symbolické regrese. Jedním z úkolů může být nastavení optimální trajektorie pro umělého mravence na stezce Santa FE. Kromě principů AP je zde osvětlené, jak AP bylo použito na nastavení trajektorie pro umělého mravence. Byly zde použity 3 evoluční algoritmy jako optimalizační nástroje - SOMA, DE a simulované žíhání (SA). Výsledky ukázaly, že je nutné zvolit vhodný optimalizační nástroj, protože simulované žíhání neobstálo tak dobře jako SOMA a DE. The paper deals with a novelty tool for symbolic regression - Analytic Programming (AP) which is able to solve various problems from the symbolic regression domain. One of the tasks for it can be a setting an optimal trajectory for an artificial ant on Santa Fe trail which is the main application of Analytic Programming in this paper. In this contribution main principles of AP are described and explained. In the second part of the article how AP was used for a setting an optimal trajectory for the artificial ant according the user requirements is in detail described. An ability to create so called programs, as well as Genetic Programming (GP) or Grammatical Evolution (GE) do, is shown in that part. AP is a superstructure of evolutionary algorithms which are necessary to run AP. In this contribution 3 evolutionary algorithms were used - Self Organizing Migrating Algorithm, Differential Evolution and Simulated Annealing. The results show that the first two used algorithms were more successful than not s The paper deals with a novelty tool for symbolic regression - Analytic Programming (AP) which is able to solve various problems from the symbolic regression domain. One of the tasks for it can be a setting an optimal trajectory for an artificial ant on Santa Fe trail which is the main application of Analytic Programming in this paper. In this contribution main principles of AP are described and explained. In the second part of the article how AP was used for a setting an optimal trajectory for the artificial ant according the user requirements is in detail described. An ability to create so called programs, as well as Genetic Programming (GP) or Grammatical Evolution (GE) do, is shown in that part. AP is a superstructure of evolutionary algorithms which are necessary to run AP. In this contribution 3 evolutionary algorithms were used - Self Organizing Migrating Algorithm, Differential Evolution and Simulated Annealing. The results show that the first two used algorithms were more successful than not s
dcterms:title
Santa Fe Trail for Artificial Ant with Analytic Programming and Three Evolutionary Algorithms Santa Fe Trail for Artificial Ant with Analytic Programming and Three Evolutionary Algorithms Stezka Santa Fe pro umělého mravence s analytickým programováním a třemi evolučními algoritmy
skos:prefLabel
Santa Fe Trail for Artificial Ant with Analytic Programming and Three Evolutionary Algorithms Stezka Santa Fe pro umělého mravence s analytickým programováním a třemi evolučními algoritmy Santa Fe Trail for Artificial Ant with Analytic Programming and Three Evolutionary Algorithms
skos:notation
RIV/70883521:28140/07:63505862!RIV08-MSM-28140___
n6:strany
334-339
n6:aktivita
n11:Z n11:P
n6:aktivity
P(GA102/05/0271), P(GA102/06/1132), Z(MSM7088352101)
n6:dodaniDat
n16:2008
n6:domaciTvurceVysledku
n8:3433390 n8:5781264
n6:druhVysledku
n17:D
n6:duvernostUdaju
n9:S
n6:entitaPredkladatele
n20:predkladatel
n6:idSjednocenehoVysledku
448875
n6:idVysledku
RIV/70883521:28140/07:63505862
n6:jazykVysledku
n19:eng
n6:klicovaSlova
Evolutionary algorithms; symbolic regression; Santa Fe
n6:klicoveSlovo
n7:Evolutionary%20algorithms n7:Santa%20Fe n7:symbolic%20regression
n6:kontrolniKodProRIV
[6F3651407B46]
n6:mistoVydani
Phuket
n6:nazevZdroje
AMS 2007
n6:obor
n18:JD
n6:pocetDomacichTvurcuVysledku
2
n6:pocetTvurcuVysledku
2
n6:projekt
n14:GA102%2F06%2F1132 n14:GA102%2F05%2F0271
n6:rokUplatneniVysledku
n16:2007
n6:tvurceVysledku
Oplatková, Zuzana Zelinka, Ivan
n6:zamer
n12:MSM7088352101
s:numberOfPages
6
n13:hasPublisher
IEEE Computer Society
n4:isbn
0-7695-2845-7
n10:organizacniJednotka
28140