. . "Evolutionary algorithms; symbolic regression; synthesis"@en . "0-7695-2799-X" . "Higher Dimensional Cost Function for Synthesis of Evolutionary Algorithms by means of Symbolic Regression"@en . "Zelinka, Ivan" . . "This contribution deals with a new idea of how to create evolutionary algorithms by means of symbolic regression and Analytic Programming. The motivation was not only to tune some existing algorithms to their better performance, but also to find a new robust evolutionary algorithm. In this study operators of Differential Evolution (DE), SelfOrganizing Migrating Algortithm (SOMA), Hill Climbing (HC) and Simulated Annealing (SA) were used during a process of Analytic Programming. The results showed that AP was able to find successful as well as the original DE or SOMA. The cost function includes not only success in unimodal and multimodal benchmark function but also rules concerned to cost function evaluations. Results were tested on 16 benchmark functions in 2D, 20 D and 100 dimensional versions, i.e. 192 test, each was 100 times repeated and each of 100 repetitions has around 200 000 cost function evaluations. The results are presented in tabelar and graphic form." . "Higher Dimensional Cost Function for Synthesis of Evolutionary Algorithms by means of Symbolic Regression"@en . . "Second Asia International Conference on Modelling and Simulation" . "370101" . . . "2"^^ . . . "Higher Dimensional Cost Function for Synthesis of Evolutionary Algorithms by means of Symbolic Regression" . "2"^^ . . . "V\u00EDcedimenzion\u00E1ln\u00ED \u00FA\u010Delov\u00E1 funkce pro synt\u00E9zu evolu\u010Dn\u00EDch algoritm\u016F pomoc\u00ED symbolick\u00E9 regrese"@cs . . "Kuala Lumpur, Malaysia" . "[06B5458A6A5C]" . "Oplatkov\u00E1, Zuzana" . . . "RIV/70883521:28140/08:63507086" . "IEEE Operations Center" . . "This contribution deals with a new idea of how to create evolutionary algorithms by means of symbolic regression and Analytic Programming. The motivation was not only to tune some existing algorithms to their better performance, but also to find a new robust evolutionary algorithm. In this study operators of Differential Evolution (DE), SelfOrganizing Migrating Algortithm (SOMA), Hill Climbing (HC) and Simulated Annealing (SA) were used during a process of Analytic Programming. The results showed that AP was able to find successful as well as the original DE or SOMA. The cost function includes not only success in unimodal and multimodal benchmark function but also rules concerned to cost function evaluations. Results were tested on 16 benchmark functions in 2D, 20 D and 100 dimensional versions, i.e. 192 test, each was 100 times repeated and each of 100 repetitions has around 200 000 cost function evaluations. The results are presented in tabelar and graphic form."@en . . "Higher Dimensional Cost Function for Synthesis of Evolutionary Algorithms by means of Symbolic Regression" . "Piscataway" . "2008-05-15+02:00"^^ . . . "28140" . . "V\u00EDcedimenzion\u00E1ln\u00ED \u00FA\u010Delov\u00E1 funkce pro synt\u00E9zu evolu\u010Dn\u00EDch algoritm\u016F pomoc\u00ED symbolick\u00E9 regrese"@cs . "P(GA102/06/1132), Z(MSM7088352101)" . . "RIV/70883521:28140/08:63507086!RIV09-GA0-28140___" . "Tento \u010Dl\u00E1nek se zab\u00FDv\u00E1 alternativn\u00EDm n\u00E1strojem pro symbolickou regresi - analytick\u00FDm programov\u00E1n\u00EDm, kter\u00E9 je schopno \u0159e\u0161it slo\u017Eit\u00E9 probl\u00E9my v oblastni symbolick\u00E9 regrese stejn\u011B jako genetick\u00E9 programov\u00E1n\u00ED \u010Di gramatick\u00E1 evoluce. Hlavn\u00EDm c\u00EDlem bylo uk\u00E1zat, jak lze syntetizovat nov\u00E9 evolu\u010Dn\u00ED algoritmy pomoc\u00ED analytick\u00E9ho programov\u00E1n\u00ED a to pro v\u00EDce prom\u011Bnn\u00FDch (20), nejen 2, kter\u00E9 byly pou\u017Eity v p\u0159edchoz\u00EDch v\u00FDzkumech."@cs . "7"^^ .