Understanding grammatical evolution: initialisation
Grammatical evolution is one of the most used variants of genetic programming, and ever since its introduction, several improvements have been suggested. One of these concerns the routine used to create the initial population. In this study, several proposed initialisation routines are compared; bas...
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| Published in | Genetic programming and evolvable machines Vol. 18; no. 4; pp. 467 - 507 |
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| Main Author | |
| Format | Journal Article |
| Language | English |
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New York
Springer US
01.12.2017
Springer Nature B.V |
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| Online Access | Get full text |
| ISSN | 1389-2576 1573-7632 |
| DOI | 10.1007/s10710-017-9309-9 |
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| Abstract | Grammatical evolution is one of the most used variants of genetic programming, and ever since its introduction, several improvements have been suggested. One of these concerns the routine used to create the initial population. In this study, several proposed initialisation routines are compared; based on a detailed analysis of the generated initial populations, and subsequent results obtained on a large set of experiments, a variant of the PTC2 algorithm is shown to consistently outperform all other routines, while a variant of random initialisation provides a good compromise between efficiency and ease of implementation. |
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| AbstractList | Grammatical evolution is one of the most used variants of genetic programming, and ever since its introduction, several improvements have been suggested. One of these concerns the routine used to create the initial population. In this study, several proposed initialisation routines are compared; based on a detailed analysis of the generated initial populations, and subsequent results obtained on a large set of experiments, a variant of the PTC2 algorithm is shown to consistently outperform all other routines, while a variant of random initialisation provides a good compromise between efficiency and ease of implementation. |
| Author | Nicolau, Miguel |
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| Cites_doi | 10.1016/j.dss.2009.05.016 10.1109/CEC.2016.7748331 10.1109/4235.873237 10.1162/evco.1997.5.4.401 10.1109/CEC.2012.6256563 10.1109/TEVC.2015.2502841 10.1007/s10710-014-9224-2 10.1007/0-387-28111-8_15 10.1016/j.neucom.2008.01.017 10.1007/s10710-012-9177-2 10.1023/A:1010024515191 10.1145/2739480.2754784 10.1007/3-540-45984-7_12 10.1007/s10710-015-9262-4 10.1109/CEC.2010.5586336 10.1023/A:1021877127167 10.1007/978-1-4614-1770-5_8 10.1007/978-3-642-12148-7_6 10.1016/j.asoc.2003.11.001 10.1007/978-3-319-55696-3_20 10.1068/b38037 10.1007/3-540-36599-0_7 10.1145/2908812.2908904 10.1007/978-3-642-12148-7_2 10.1007/978-3-319-45823-6_27 10.1145/1569901.1570041 10.1007/978-3-540-24650-3_28 10.1007/978-3-662-43631-8 10.1109/TEVC.2008.926486 10.1109/TCIAIG.2016.2543661 |
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| Keywords | Representation bias Design Symbolic regression Grammatical evolution Classification Initialisation Tree creation |
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Mach.201415440343110.1007/s10710-014-9224-2 KeijzerMRy F Rothlauf (9309_CR40) 2006 MF Korns (9309_CR48) 2011 9309_CR50 9309_CR52 9309_CR12 9309_CR13 9309_CR18 PA Whigham (9309_CR10) 1995 WB Langdon (9309_CR20) 2000; 1 DR White (9309_CR23) 2013; 14 A Thorhauer (9309_CR14) 2016 A Brabazon (9309_CR44) 2015 S Luke (9309_CR22) 2000; 4 M Fenton (9309_CR15) 2016; 20 M Keijzer (9309_CR28) 2002 A Brabazon (9309_CR31) 2006 EF Crane (9309_CR19) 2006 M O’Neill (9309_CR25) 2004 9309_CR43 9309_CR41 D Fagan (9309_CR27) 2010 M Nicolau (9309_CR37) 2012 9309_CR3 C Ryan (9309_CR2) 1998 9309_CR5 9309_CR6 9309_CR8 M Keijzer (9309_CR45) 2003 JR Koza (9309_CR1) 1992 N Lourenço (9309_CR29) 2016; 17 J McDermott (9309_CR32) 2012; 39 L Pagie (9309_CR46) 1997; 5 M O’Neill (9309_CR11) 2003; 4 RMA Azad (9309_CR30) 2004; 4 P Cortez (9309_CR49) 2007 N Lourenço (9309_CR42) 2017 9309_CR34 M Hemberg (9309_CR35) 2004 DJ Montana (9309_CR9) 1995; 3 J Tavares (9309_CR38) 2012 M O’Neill (9309_CR16) 2003 C Ryan (9309_CR39) 2003 T Soule (9309_CR21) 1997 J Byrne (9309_CR4) 2010 R Harper (9309_CR33) 2014; 15 9309_CR26 9309_CR24 JE Murphy (9309_CR36) 2009 S Forstenlechner (9309_CR7) 2016 EJ Vladislavleva (9309_CR47) 2009; 13 I Tsoulos (9309_CR17) 2008; 72 P Cortez (9309_CR51) 2009; 47 |
| References_xml | – reference: M. Nicolau, Automatic grammar complexity reduction in grammatical evolution, in Genetic and Evolutionary Computation—GECCO 2004, Genetic and Evolutionary Computation Conference, Seattle, WA, USA, June 26–30, 2004, Workshops, Proceedings, ed. by R. Poli, S. Cagnoni, M. Keijzer, E. Costa, F. Pereira, G. Raidl, S.C. Upton, D.E. Goldberg, H. Lipson, E. de Jong, J.R. Koza, H. Suzuki, H. Sawai, I. Parmee, M. Pelikan, K. Sastry, D. Thierens, W. Stolzmann, P.L. Lanzi, S.W. Wilson, M. O’Neill, C. Ryan, T. Yu, J.F. Miller, I. Garibay, G. Holifield, A.S. Wu, T. Riopka, M.M. Meysenburg, A.W. Wright, N. Richter, J.H. Moore, M.D. Ritchie, L. Davis, R. Roy, M. Jakiela (2004) – reference: M. Nicolau, M. O’Neill, A. 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| Title | Understanding grammatical evolution: initialisation |
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