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Analytical Programming - a Novel Approach for Evolutionary Synthesis of Symbolic Structures

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dc.title Analytical Programming - a Novel Approach for Evolutionary Synthesis of Symbolic Structures en
dc.contributor.author Zelinka, Ivan
dc.contributor.author Davendra, Donald David
dc.contributor.author Šenkeřík, Roman
dc.contributor.author Jašek, Roman
dc.contributor.author Oplatková, Zuzana
dc.relation.ispartof Evolutionary Algorithms
dc.identifier.isbn 978-953-307-171-8
dc.date.issued 2011
dc.event.location Rijeka
utb.event.state-en Croatia
utb.event.state-cs Chorvatsko
dc.type bookPart
dc.language.iso en
dc.publisher InTech
dc.relation.uri http://www.intechopen.com/books/evolutionary-algorithms/analytical-programming-a-novel-approach-for-evolutionary-synthesis-of-symbolic-structures
dc.subject Analytic programming en
dc.subject optimization en
dc.subject evolutionary algorithms en
dc.description.abstract This chapter discusses an alternative approach for symbolic structures and solutions synthesis and demonstrates a comparison with other methods, for example Genetic Programming (GP) or Grammatical Evolution (GE). Generally, there are two well known methods, which can be used for symbolic structures synthesis by means of computers. The first one is called GP and the other is GE. Another interesting research was carried out by Artificial Immune Systems (AIS) or/and systems, which do not use tree structures like linear GP and other similar algorithm like Multi Expression Programming (MEP), etc. In this chapter, a different method called Analytic Programming (AP), is presented. AP is a grammar free algorithmic superstructure, which can be used by any programming language and also by any arbitrary Evolutionary Algorithm (EA) or another class of numerical optimization method. This chapter describes not only theoretical principles of AP, but also its comparative study with selected well known case examples from GP as well as applications on synthesis of: controller, systems of deterministic chaos, electronics circuits, etc. For simulation purposes, AP has been co-joined with EA’s like Differential Evolution (DE), Self-Organising Migrating Algorithm (SOMA), Genetic Algorithms (GA) and Simulated Annealing (SA). All case studies has been carefully prepared and repeated in order to get valid statistical data for proper conclusions. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1005992
utb.identifier.rivid RIV/70883521:28140/11:43866620!RIV12-MSM-28140___
utb.identifier.obdid 43866744
utb.identifier.wok 000386477200010
utb.source c-riv
dc.date.accessioned 2016-04-28T10:37:30Z
dc.date.available 2016-04-28T10:37:30Z
dc.description.sponsorship P(ED2.1.00/03.0089), P(GA102/09/1680), S, Z(MSM7088352101)
dc.format.extent 584
dc.rights Attribution-NonCommercial-ShareAlike 3.0 Unported
dc.rights.uri http://creativecommons.org/licenses/by-nc-sa/3.0/
dc.rights.access openAccess
utb.contributor.internalauthor Davendra, Donald David
utb.contributor.internalauthor Šenkeřík, Roman
utb.contributor.internalauthor Jašek, Roman
utb.contributor.internalauthor Oplatková, Zuzana
riv.obor IN
utb.fulltext.affiliation Ivan Zelinka1, Donald Davendra2, Roman Senkerik3, Roman Jasek4 and Zuzana Oplatkova5 1,2,3,4,5Tomas Bata University in Zlin, Faculty of Applied Informatics, Nad Stranemi 4511, Zlin, 76001 1Department of Computer Science, Faculty of Electrical Engineering and Computer Science VSB-TUO, 17. listopadu 15, 708 33 Ostrava-Poruba Czech Republic
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