Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Memetic Computing
Erschienen in: Memetic Computing 4/2020

13.10.2020 | Regular Research Paper

An efficient memetic genetic programming framework for symbolic regression

verfasst von: Tiantian Cheng, Jinghui Zhong

Erschienen in: Memetic Computing | Ausgabe 4/2020

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Background

Symbolic regression is one of the most common applications of genetic programming (GP), which is a popular evolutionary algorithm in automatic computer program generation. Despite existing success of GP on symbolic regression, the accuracy and efficiency of GP can still be improved especially on complicated symbolic regression problems, enabling GP to be applied to more fields.

Purpose

This paper proposes a novel memetic GP framework to improve the accuracy and search efficiency of GP on complicated symbolic regression problems. The proposed framework consists of two components: feature construction and feature combination. The first component focuses on constructing diverse features. The second component aims to filter redundant features and linearly combines these independent features.

Methods

The first component (feature construction) focuses on constructing polynomial features derived from polynomial functions, and evolves features by a GP solver. In addition, a gradient-based nonlinear least squares algorithm named Levenberg-Marquardt (LM) is embedded in the second component (feature combination) to locally adjust the weights of independent features. A filtering mechanism is put forward to discard redundant features in the second component. Hence, the polynomial features and evolved features can work together in the framework to improve the performance of GP.

Results

Experimental results demonstrate that the proposed framework offers enhanced performance compared with several state-of-the-art algorithms in terms of accuracy and search efficiency on nine benchmark regression problems and three real-world regression problems.

Conclusion

In this study, a novel memetic genetic programming framework is proposed to improve the performance of GP on symbolic regression. Experimental results demonstrate that the proposed framework can improve the accuracy and search efficiency of GP on complicated symbolic regression problems compared with four state-of-the-art algorithms.
Vorheriger Artikel Feature selection based bee swarm meta-heuristic approach for combinatorial optimisation problems: a case-study on MaxSAT
Nächster Artikel Designing optimal combination therapy for personalised glioma treatment

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Fußnoten
Literatur
1.
Arnaldo I, Krawiec K, O’Reilly UM (2014) Multiple regression genetic programming. In: Proceedings of the 2014 annual conference on genetic and evolutionary computation. ACM, pp 879–886
2.
Arnaldo I, O’Reilly UM, Veeramachaneni K (2015) Building predictive models via feature synthesis. In: Proceedings of the 2015 annual conference on genetic and evolutionary computation. ACM, pp 983–990
3.
Barrero, DF (2011) Relibility of performance measures in tree-based genetic programming: a study on Koza’s computational effort. Ph.D. thesis, School of Computing of the University of Alcala
4.
Beadle L, Johnson CG (2008) Semantically driven crossover in genetic programming. In: IEEE congress on evolutionary computation. IEEE, pp 111–116
5.
Beadle L, Johnson CG (2009) Semantically driven mutation in genetic programming. In: IEEE congress on evolutionary computation. IEEE, pp 1336–1342
6.
Brameier MF, Banzhaf W (2007) Linear genetic programming. Springer, BerlinMATH
7.
8.
9.
10.
Cortez P, Cerdeira A, Almeida F, Matos T, Reis J (2009) Modeling wine preferences by data mining from physicochemical properties. Decis Support Syst 47(4):547–553CrossRef
11.
Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Trans Evolut Comput 6(2):182–197CrossRef
12.
Eremeev AV, Kovalenko YV (2019) A memetic algorithm with optimal recombination for the asymmetric travelling salesman problem. Memet Comput 12(1):23–36CrossRef
13.
Espejo PG, Ventura S, Herrera F (2010) A survey on the application of genetic programming to classification. IEEE Trans Syst Man Cybern C (Appl Rev) 40(2):121–144CrossRef
14.
15.
16.
17.
Hinchliffe M, Hiden H, McKay B, Willis M, Tham M, Barton G (1996) Modelling chemical process systems using a multi-gene genetic programming algorithm. In: Koza JR (ed) Late breaking papers at the genetic programming 1996 conference Stanford University July 28–31, 1996. Stanford Bookstore, Stanford University, CA, USA, pp 56–65
18.
19.
Kommenda M, Kronberger G, Winkler S, Affenzeller M, Wagner S (2013) Effects of constant optimization by nonlinear least squares minimization in symbolic regression. In: Proceedings of the 15th annual conference companion on Genetic and evolutionary computation. ACM, pp 1121–1128
20.
Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection, vol 1. MIT press, CambridgeMATH
21.
Krawiec K, Lichocki P (2009) Approximating geometric crossover in semantic space. In: Proceedings of the 11th annual conference on genetic and evolutionary computation. ACM, pp 987–994
22.
Marquardt DW (1963) An algorithm for least-squares estimation of nonlinear parameters. J Soc Ind Appl Math 11(2):431–441MathSciNetCrossRef
23.
McConaghy T (2011) Ffx: fast, scalable, deterministic symbolic regression technology. In: Genetic programming theory and practice IX. Springer, pp 235–260
24.
McDermott J, White DR, Luke S, Manzoni L, Castelli M, Vanneschi L, Jaskowski W, Krawiec K, Harper R, De Jong K (2012) Genetic programming needs better benchmarks. In: Proceedings of the 14th annual conference on genetic and evolutionary computation. ACM, pp 791–798
25.
Meuth R, Lim MH, Ong YS, Wunsch DC (2009) A proposition on memes and meta-memes in computing for higher-order learning. Memet Comput 1(2):85–100CrossRef
26.
Miller JF (2011) Cartesian genetic programming. Springer, Berlin, pp 17–34CrossRef
27.
Moraglio A, Krawiec K, Johnson CG (2012) Geometric semantic genetic programming. In: International conference on parallel problem solving from nature. Springer, pp 21–31
28.
Muñoz L, Trujillo L, Silva S, Castelli M, Vanneschi L (2019) Evolving multidimensional transformations for symbolic regression with m3gp. Memet Comput 11(2):111–126CrossRef
29.
Nguyen QU, Nguyen XH, O’Neill M (2009) Semantic aware crossover for genetic programming: the case for real-valued function regression. In: European conference on genetic programming. Springer, pp 292–302
30.
31.
32.
Orzechowski P, Cava WL, Moore JH (2018) Where are we now? A large benchmark study of recent symbolic regression methods. CoRR arXiv:​1804.​09331
33.
34.
Price K, Storn R (1995) Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous space. Technical report, International Computer Science Institue, Berkley
35.
Ryan C, Keijzer M (2003) An analysis of diversity of constants of genetic programming. In: European conference on genetic programming. Springer, pp 404–413
36.
37.
Searson DP, Leahy DE, Willis MJ (2010) Gptips: an open source genetic programming toolbox for multigene symbolic regression. In: Proceedings of the international multiconference of engineers and computer scientists, vol 1. Citeseer, pp 77–80
38.
Smits GF, Kotanchek M (2005) Pareto-front exploitation in symbolic regression. In: Genetic programming theory and practice II. Springer, pp 283–299
39.
Suganuma M, Shirakawa S, Nagao, T (2017) A genetic programming approach to designing convolutional neural network architectures. In: Proceedings of the genetic and evolutionary computation conference, pp 497–504
40.
Tan LT, Chen WN, Zhang J (2018) A histogram estimation of distribution algorithm for resource scheduling. In: Proceedings of the genetic and evolutionary computation conference companion. ACM, pp 143–144
41.
Tibshirani R (1996) Regression shrinkage and selection via the lasso. J R Stat Soc Ser B (Methodol) 58:267–288MathSciNetMATH
42.
Topchy A, Punch WF (2001) Faster genetic programming based on local gradient search of numeric leaf values. In: Proceedings of the 3rd annual conference on genetic and evolutionary computation. Morgan Kaufmann Publishers Inc., pp 155–162
43.
Tsanas A, Xifara A (2012) Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools. Energy Build 49:560–567CrossRef
44.
Uy NQ, Hoai NX, O’Neill M (2009) Semantics based mutation in genetic programming: the case for real-valued symbolic regression. In: 15th International conference on soft computing, Mendel, vol 9, pp 73–91
45.
Uy NQ, Hoai NX, O’Neill M, McKay RI, Galván-López E (2011) Semantically-based crossover in genetic programming: application to real-valued symbolic regression. Genet Program Evolv Mach 12(2):91–119CrossRef
46.
Vanneschi L, Mauri G, Valsecchi A, Cagnoni S (2006) Heterogeneous cooperative coevolution: strategies of integration between gp and ga. In: Proceedings of the 8th annual conference on genetic and evolutionary computation. ACM, pp 361–368
47.
Virgolin M, Alderliesten T, Bosman PAN (2019) Linear scaling with and within semantic backpropagation-based genetic programming for symbolic regression. In: Proceedings of the genetic and evolutionary computation conference, GECCO 2019, Prague, Czech Republic, July 13–17, 2019, pp 1084–1092
48.
Vladislavleva E, Smits G, Den Hertog D (2010) On the importance of data balancing for symbolic regression. IEEE Trans Evolut Comput 14(2):252–277CrossRef
49.
50.
Wieloch B, Krawiec K (2013) Running programs backwards: instruction inversion for effective search in semantic spaces. In: Proceedings of the 15th annual conference on genetic and evolutionary computation
51.
52.
53.
Zhang Q, Zhou C, Xiao W, Nelson PC (2007) Improving gene expression programming performance by using differential evolution. In: Sixth international conference on machine learning and applications (ICMLA 2007). IEEE, pp 31–37
54.
55.
Zhong J, Ong YS, Cai W (2016) Self-learning gene expression programming. IEEE Trans Evol Comput 20(1):65–80CrossRef
Metadaten
Titel
An efficient memetic genetic programming framework for symbolic regression
verfasst von
Tiantian Cheng
Jinghui Zhong
Publikationsdatum
13.10.2020
Verlag
Springer Berlin Heidelberg
Erschienen in
Memetic Computing / Ausgabe 4/2020
Print ISSN: 1865-9284
Elektronische ISSN: 1865-9292
DOI
https://doi.org/10.1007/s12293-020-00311-8

Weitere Artikel der Ausgabe 4/2020

Memetic Computing 4/2020 Zur Ausgabe

Regular Research Paper

A unified linear convergence analysis of k-SVD

Regular Research Paper

Evolution of biocoenosis through symbiosis with fitness approximation for many-tasking optimization

Regular Research Paper

Multi-task gradient descent for multi-task learning

Regular Research Paper

Chaotic-based grey wolf optimizer for numerical and engineering optimization problems

Regular Research Paper

Cooperative co-evolutionary comprehensive learning particle swarm optimizer for formulation design of explosive simulant

Regular Research Paper

Feature selection based bee swarm meta-heuristic approach for combinatorial optimisation problems: a case-study on MaxSAT