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Analytické Programování - Symbolická regrese s využitím jakéhokoli evolučního algoritmu

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dc.title Analytické Programování - Symbolická regrese s využitím jakéhokoli evolučního algoritmu cs
dc.title Analytic Programming - Symbolic Regression by Means of Arbitrary Evolutionary Algorithms en
dc.contributor.author Zelinka, Ivan
dc.contributor.author Oplatková, Zuzana
dc.contributor.author Nolle, Lars
dc.relation.ispartof International Journal of Simulation, Systems, Science and Technology
dc.identifier.issn 1473-8031 Scopus Sources, Sherpa/RoMEO, JCR
dc.date.issued 2005
utb.relation.volume 6
utb.relation.issue 9
dc.citation.spage 44
dc.citation.epage 56
dc.type article
dc.language.iso en
dc.publisher Society for Promoting International Research and Innovation (SPIRI) en
dc.relation.uri http://ijssst.info/Vol-06/No-9/cover-06-9.htm
dc.subject symbolická regrese cs
dc.subject genetické programování cs
dc.subject gramatická evoluce cs
dc.subject analytické programování cs
dc.subject SOMA cs
dc.subject symbolic regression en
dc.subject genetic programming en
dc.subject grammar evolution en
dc.subject analytic programming en
dc.subject SOMA en
dc.description.abstract Tento příspěvek popisuje analytické programování, novou metodu, která umožňuje řešit různé problémy z pole symbolické regrese. Symbolická regrese byla poprvé navržena J.R. Kozou v genetickém programování a C. Ryanem v gramatické evoluci. Tento příspěvek vysvětluje hlavní principy analytického programování a demonstruje jeho schopnost syntetizovat vhodná řešení, zvané programy. Pak je srovnána struktura s genetickým programováním a gramatickou evolucí. Po teoretické části, komparativní studie zaměřená na Boolovské problémy k-symetrické a k-paritní s Kozovým genetickým programováním a analytickým programováním. Zde jsou použity dva evoluční algoritmy: diferenciální evoluce a samoorganizující se migrační algoritmus. Tato komparativní studie Boolovských k-symetrických a k-paritních problémů je pokračováním předchozí studie provedené analytickým programováním v minulosti. cs
dc.description.abstract This contribution introduces analytical programming, a novel method that allows solving various problems from the symbolic regression domain. Symbolic regression was first proposed by J. R. Koza in his genetic programming and by C. Ryan in grammatical evolution. This contribution explains the main principles of analytic programming, and demonstrates its ability to synthesize suitable solutions, called programs. It is then compared in its structure with genetic programming and grammatical evolution. After theoretical part, a comparative study concerned with Boolean k-symmetry and k-even problems from Koza's genetic programming domain is done with analytical programming. Here, two evolutionary algorithms are used with analytical programming: differential evolution and self-organizing migrating algorithm. Boolean k-symmetry and k-even problems comparative study here are continuation of previous comparative studies done by analytic programming in the past. en
utb.faculty Faculty of Technology
dc.identifier.uri http://hdl.handle.net/10563/1000500
utb.identifier.rivid RIV/70883521:28110/05:63504197
utb.identifier.obdid 14053624
utb.identifier.scopus 2-s2.0-84925043612
utb.source j-riv
utb.contributor.internalauthor Zelinka, Ivan
utb.contributor.internalauthor Oplatková, Zuzana
utb.fulltext.affiliation IVAN ZELINKA ZUZANA OPLATKOVA Institute of Control Processing and Information Technologies Faculty of Technology Tomas Bata University in Zlin Mostni 5139 Zlin, Czech Republic Email: {zelinka,oplatkova}@ft.utb.cz LARS NOLLE School of Computing and Mathematics The Nottingham Trent University Burton Street Nottingham, NG1 4BU, UK Email: Lars.nolle@ntu.ac.uk
utb.fulltext.dates -
utb.fulltext.references Johnson C. G. 2003, Artificial immune systems programming for symbolic regression, In C. Ryan, T. Soule, M. Keijzer, E. Tsang, R. Poli, and E. Costa, editors, Genetic Programming: 6th European Conference, LNCS 2610, p. 345-353. Koza J. R., Keane M. A., Streeter M. J. 2003, Evolving Inventions, Scientific American, February, p. 40-47. Koza J.R. 1998, Genetic Programming II, MIT Press. Koza J.R., Bennet F.H., Andre D., Keane M., 1999, Genetic Programming III, Morgan Kaufnamm pub. Lampinen J., Zelinka I. 1999, New Ideas in Optimization - Mechanical Engineering Design Optimization by Differential Evolution. Volume 1. London : McGraw-Hill. 20 p. O'Neill M. and Ryan C. 2002, Grammatical Evolution. Evolutionary Automatic Programming in an Arbitrary Language. Kluwer Academic Publishers. O'Sullivan J., Conor R. 2002, An Investigation into the Use of Different Search Strategies with Grammatical Evolution Proceedings of the 5th European Conference on Genetic Programming, p.268 - 277, Springer-Verlag London, UK. Price K. 1999, An Introduction to Differential Evolution, in New Ideas in Optimization, D. Corne, M. Dorigo and F. Glover, Eds., s. 79–108, McGraw-Hill, London, UK. Ryan C., Collins J.J., O'Neill 1998, M. Grammatical Evolution: Evolving Programs for an Arbitrary Language. Lecture Notes in Computer Science 1391. First European Workshop on Genetic Programming. Zelinka I., 2002 a, Analytic programming by Means of Soma Algorithm. Mendel ’02, In: Proc. 8th International Conference on Soft Computing Mendel’02, Brno, Czech Republic, 93-101. Zelinka I., 2002 b, Analytic programming by Means of Soma Algorithm. ICICIS’02, First International Conference on Intelligent Computing and Information Systems, Egypt, Cairo. Zelinka I., Oplatkova Z., 2003, Analytic programming – Comparative Study. CIRAS’03, The second International Conference on Computational Intelligence, Robotics, and Autonomous Systems, Singapore. Zelinka I., Oplatkova Z., 2004, Boolean Parity Function Synthesis by Means of Arbitrarry Evolutionary Algorithms - Comparative Study", In: 8th World Multiconference on Systemics, Cybernetics and Informatics (SCI 2004), Orlando, USA, in July 18-21, in print. Zelinka I., Oplatkova Z., Nolle L., 2004, Boolean Symmetry Function Synthesis by Means of Arbitrarry Evolutionary Algorithms - Comparative Study", In: 18th European Simulation Multiconference (ESM 2004), Magdeburg, Germany, June 13-16. Zelinka Ivan, 2004, SOMA – Self Organizing Migrating Algorithm“,Chapter 7, 33 p. in: B.V. Babu, G. Onwubolu (eds), New Optimization Techniques in Engineering, Springer-Verlag.
utb.fulltext.sponsorship This work was supported by grant No. MSM 7088352101 of the Ministry of Education of the Czech Republic and by grants of the Grant Agency of the Czech Republic GACR 102/03/0070 and GACR 102/02/0204.
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