Behavioral Search Drivers for Genetic Programing

  • Krzysztof Krawiec
  • Una-May O’Reilly
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8599)


Synthesizing a program with the desired input-output behavior by means of genetic programming is an iterative process that needs appropriate guidance. That guidance is conventionally provided by a fitness function that measures the conformance of program output with the desired output. Contrary to widely adopted stance, there is no evidence that this quality measure is the best choice; alternative search drivers may exist that make search more effective. This study proposes and investigates a new family of behavioral search drivers, which inspect not only final program output, but also program behavior meant as the partial results it arrives at while executed.


Genetic Programing Candidate Solution Search Operator Single Point Mutation Behavioral Evaluation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Krawiec, K., Swan, J.: Pattern-guided genetic programming. In: Blem, C., et al. (eds.) GECCO 2013: Proceeding of the Fifteenth Annual Conference on Genetic and Evolutionary Computation Conference, Amsterdam, The Netherlands, pp. 949–956. ACM (2013)Google Scholar
  2. 2.
    Quinlan, J.: C4.5: Programs for machine learning. Morgan Kaufmann (1992)Google Scholar
  3. 3.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: an update. SIGKDD Explor. Newsl. 11(1), 10–18 (2009)CrossRefGoogle Scholar
  4. 4.
    Quinlan, J.R., Rivest, R.L.: Inferring decision trees using the minimum description length principle. Inf. Comput. 80(3), 227–248 (1989)CrossRefzbMATHMathSciNetGoogle Scholar
  5. 5.
    Smith, R., Forrest, S., Perelson, A.: Searching for diverse, cooperative populations with genetic algorithms. Evolutionary Computation 1(2) (1993)Google Scholar
  6. 6.
    Krawiec, K., Lichocki, P.: Using co-solvability to model and exploit synergetic effects in evolution. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds.) PPSN XI. LNCS, vol. 6239, pp. 492–501. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  7. 7.
    Tomassini, M., Vanneschi, L., Collard, P., Clergue, M.: A study of fitness distance correlation as a difficulty measure in genetic programming. Evolutionary Computation 13(2), 213–239 (2005)CrossRefGoogle Scholar
  8. 8.
    Iba, H., Sato, T., de Garis, H.: System identification approach to genetic programming. In: Proceedings of the 1994 IEEE World Congress on Computational Intelligence, Orlando, Florida, USA, vol. 1, pp. 401–406. IEEE Press (1994)Google Scholar
  9. 9.
    Zhang, B.T., Mühlenbein, H.: Balancing accuracy and parsimony in genetic programming. Evolutionary Computation 3(1), 17–38 (1995)CrossRefGoogle Scholar
  10. 10.
    McPhee, N.F., Ohs, B., Hutchison, T.: Semantic building blocks in genetic programming. In: O’Neill, M., Vanneschi, L., Gustafson, S., Esparcia Alcázar, A.I., De Falco, I., Della Cioppa, A., Tarantino, E. (eds.) EuroGP 2008. LNCS, vol. 4971, pp. 134–145. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  11. 11.
    Krawiec, K., Bhanu, B.: Visual learning by evolutionary and coevolutionary feature synthesis. IEEE Transactions on Evolutionary Computation 11(5), 635–650 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Krzysztof Krawiec
    • 1
  • Una-May O’Reilly
    • 2
  1. 1.Poznan University of TechnologyPoznańPoland
  2. 2.Computer Science and Artificial Intelligence LaboratoryMITCambridgeUSA

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