Skip to main content
SpringerLink
Log in

A Framework for Knowledge Integrated Evolutionary Algorithms

  • Conference paper
  • First Online:
Applications of Evolutionary Computation (EvoApplications 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10199))

Included in the following conference series:

Abstract

One of the main reasons for the success of Evolutionary Algorithms (EAs) is their general-purposeness, i.e. the fact that they can be applied in a straight forward manner to a broad range of optimization problems, without any specific prior knowledge. On the other hand, it has been shown that incorporating a priori knowledge, such as expert knowledge or empirical findings, can significantly improve the performance of an EA. However, integrating knowledge in EAs poses numerous challenges. It is often the case that the features of the search space are unknown, hence any knowledge associated with the search space properties can be hardly used. In addition, a priori knowledge is typically problem-specific and hard to generalize. In this paper, we propose a framework, called Knowledge Integrated Evolutionary Algorithm (KIEA), which facilitates the integration of existing knowledge into EAs. Notably, the KIEA framework is EA-agnostic, i.e. it works with any evolutionary algorithm, problem-independent, i.e. it is not dedicated to a specific type of problems and expandable, i.e. its knowledge base can grow over time. Furthermore, the framework integrates knowledge while the EA is running, thus optimizing the consumption of computational power. In the preliminary experiments shown here, we observe that the KIEA framework produces in the worst case an 80% improvement on the converge time, w.r.t. the corresponding “knowledge-free” EA counterpart.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Notes

  1. 1.

    It should be noted, however, that some literature considers the crossover operator as an exploitation mechanism. Generally, mutation and crossover have an effect on both exploration and exploitation, although this effect varies depending on the implementation and the fitness landscape at hand.

References

  1. Koza, J.R., Keane, M.A., Streeter, M.J.: What’s ai done for me lately? genetic programming’s human-competitive results. IEEE Intell. Syst. 3, 25–31 (2003)

    Article  Google Scholar 

  2. Arcuri, A., Yao, X.: Co-evolutionary automatic programming for software development. Inf. Sci. 259, 412–432 (2014)

    Article  Google Scholar 

  3. Squillero, G.: MicroGP - an evolutionary assembly program generator. Program. Evol. Mach. 6(3), 247–263 (2005)

    Article  Google Scholar 

  4. Hornby, G.S., Globus, A., Linden, D.S., Lohn, J.D.: Automated antenna design with evolutionary algorithms. In: AIAA Space, pp. 19–21 (2006)

    Google Scholar 

  5. Lohn, J.D., Linden, D.S., Hornby, G.S., Kraus, W.F., Rodriguez-Arroyo, A.: Evolutionary design of an X-band antenna for NASA’s space technology 5 mission. In: null, vol. 155. IEEE (2003)

    Google Scholar 

  6. Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1(1), 67–82 (1997)

    Article  Google Scholar 

  7. Črepinšek, M., Liu, S.H., Mernik, M.: Exploration and exploitation in evolutionary algorithms: a survey. ACM Comput. Surv. (CSUR) 45(3), 35 (2013)

    MATH  Google Scholar 

  8. Bäck, T.: Selective pressure in evolutionary algorithms: A characterization of selection mechanisms. In: Proceedings of the First IEEE Conference on Evolutionary Computation, IEEE World Congress on Computational Intelligence, 57–62. IEEE (1994)

    Google Scholar 

  9. Gates, G.H., Merkle, L.D., Lamont, G.B., Pachter, R.: Simple genetic algorithm parameter selection for protein structure prediction. In: IEEE International Conference on Evolutionary Computation, vol. 2, pp. 620–624. IEEE (1995)

    Google Scholar 

  10. Yang, M., Cai, Z., Li, C., Guan, J.: An improved adaptive differential evolution algorithm with population adaptation. In: Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation, pp. 145–152. ACM (2013)

    Google Scholar 

  11. Caorsi, S., Massa, A., Pastorino, M., Randazzo, A.: Optimization of the difference patterns for monopulse antennas by a hybrid real/integer-coded differential evolution method. IEEE Trans. Antenna Propag. 53(1), 372–376 (2005)

    Article  Google Scholar 

  12. Eiben, A.E., Hinterding, R., Michalewicz, Z.: Parameter control in evolutionary algorithms. IEEE Trans. Evol. Comput. 3(2), 124–141 (1999)

    Article  Google Scholar 

  13. Eiben, A.E., Schippers, C.A.: On evolutionary exploration and exploitation. Fundamenta Informaticae 35(1–4), 35–50 (1998)

    MATH  Google Scholar 

  14. Harik, G.R., Lobo, F.G.: A parameter-less genetic algorithm. In: Proceedings of the 1st Annual Conference on Genetic and Evolutionary Computation-Volume 1, pp. 258–265. Morgan Kaufmann Publishers Inc. (1999)

    Google Scholar 

  15. Harik, G.R., Lobo, F.G., Goldberg, D.E.: The compact genetic algorithm. IEEE Trans. Evol. Comput. 3(4), 287–297 (1999)

    Article  Google Scholar 

  16. Iacca, G., Mallipeddi, R., Mininno, E., Neri, F., Suganthan, P.N.: Super-fit and population size reduction in compact differential evolution. In: IEEE Workshop on Memetic Computing (MC), pp. 1–8. IEEE (2011)

    Google Scholar 

  17. Kononova, A.V., Corne, D.W., Wilde, P., Shneer, V., Caraffini, F.: Structural bias in population-based algorithms. Inf. Sci. 298, 468–490 (2015)

    Article  Google Scholar 

  18. Beyer, H.G., Schwefel, H.P.: Evolution strategies-a comprehensive introduction. Nat. Comput. 1(1), 3–52 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  19. Casas, N.: Genetic algorithms for multimodal optimization: a review. arXiv preprint arXiv:1508.05342 (2015)

  20. Miller, B.L., Shaw, M.J.: Genetic algorithms with dynamic niche sharing for multimodal function optimization. In: Proceedings of IEEE International Conference on Evolutionary Computation, pp. 786–791. IEEE (1996)

    Google Scholar 

  21. Sareni, B., Krahenbuhl, L.: Fitness sharing and niching methods revisited. IEEE Trans. Evol. Comput. 2(3), 97–106 (1998)

    Article  Google Scholar 

  22. Asmus, J., Borchmann, D., Sbalzarini, I.F., Walther, D.: Towards an FCA-based recommender system for black-box optimization. In: Workshop Notes, p. 35 (2014)

    Google Scholar 

  23. Muñoz, M.A., Kirley, M., Halgamuge, S.K.: A meta-learning prediction model of algorithm performance for continuous optimization problems. In: Coello, C.A.C., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds.) PPSN 2012. LNCS, vol. 7491, pp. 226–235. Springer, Heidelberg (2012). doi:10.1007/978-3-642-32937-1_23

    Chapter  Google Scholar 

  24. Picek, S., Jakobovic, D.: From fitness landscape to crossover operator choice. In: Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation, pp. 815–822. ACM (2014)

    Google Scholar 

  25. Min, K., Kao, M.-Y., Zhu, H.: The closest pair problem under the hamming metric. In: Ngo, H.Q. (ed.) COCOON 2009. LNCS, vol. 5609, pp. 205–214. Springer, Heidelberg (2009). doi:10.1007/978-3-642-02882-3_21

    Chapter  Google Scholar 

Download references

Acknowledgments

This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 665347. We also gratefully acknowledge the computational resources provided by RWTH Compute Cluster from RWTH Aachen University under project RWTH0118.

Author information

Authors and Affiliations

  1. Chair for Integrated Signal Processing Systems, RWTH Aachen University, 52056, Aachen, Germany

    Ahmed Hallawa & Gerd Ascheid

  2. INCAS3, P.O. Box 797, 9400 AT, Assen, The Netherlands

    Anil Yaman & Giovanni Iacca

  3. Department of Mathematics and Computer Science, Eindhoven University of Technology, P.O. Box 513, 5600 MB, Eindhoven, The Netherlands

    Anil Yaman

Authors
  1. Ahmed Hallawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anil Yaman
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Giovanni Iacca
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Gerd Ascheid
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ahmed Hallawa .

Editor information

Editors and Affiliations

  1. Politecnico di Torino, Turin, Italy

    Giovanni Squillero

  2. Edinburgh Napier University, Edinburgh, United Kingdom

    Kevin Sim

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Hallawa, A., Yaman, A., Iacca, G., Ascheid, G. (2017). A Framework for Knowledge Integrated Evolutionary Algorithms. In: Squillero, G., Sim, K. (eds) Applications of Evolutionary Computation. EvoApplications 2017. Lecture Notes in Computer Science(), vol 10199. Springer, Cham. https://doi.org/10.1007/978-3-319-55849-3_42

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-55849-3_42

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-55848-6

  • Online ISBN: 978-3-319-55849-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation