Skip to main content
SpringerLink
Log in

A semantic network-based evolutionary algorithm for computational creativity

  • Special Issue
  • Published:
Evolutionary Intelligence Aims and scope Submit manuscript

Abstract

We introduce a novel evolutionary algorithm (EA) with a semantic network-based representation. For enabling this, we establish new formulations of EA variation operators, crossover and mutation, that we adapt to work on semantic networks. The algorithm employs commonsense reasoning to ensure all operations preserve the meaningfulness of the networks, using ConceptNet and WordNet knowledge bases. The algorithm can be interpreted as a novel memetic algorithm (MA), given that (1) individuals represent pieces of information that undergo evolution, as in the original sense of memetics as it was introduced by Dawkins; and (2) this is different from existing MA, where the word “memetic” has been used as a synonym for local refinement after global optimization. For evaluating the approach, we introduce an analogical similarity-based fitness measure that is computed through structure mapping. This setup enables the open-ended generation of networks analogous to a given base network.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Notes

  1. From a biological perspective, this sense emphasizes the effect of society, culture, and learning on the survival of individuals on top of their physical traits emerging through genetic evolution. An example would be the use of knowledge and technology by the human species to survive in diverse environments, far beyond the physical capabilities available to them solely by the human anatomy.

  2. Or, information, idea, or belief.

  3. Quoting Dawkins [9]: “Examples of memes are tunes, ideas, catch-phrases, clothes fashions, ways of making pots or of building arches. Just as genes propagate themselves in the gene pool by leaping from body to body via sperms or eggs, so memes propagate themselves in the meme pool by leaping from brain to brain...”.

  4. This is in stark contrast with approaches such as GA, where a crossover operation of identical parents would yield identical offspring due to the linear nature of the representation

  5. http://www.cyc.com/.

  6. http://conceptnet5.media.mit.edu/.

  7. http://rtw.ml.cmu.edu/rtw/.

  8. http://wordnet.princeton.edu.

  9. http://www.wikimedia.org/.

  10. http://dbpedia.org/.

  11. The default reliability score for a statement is 1 [19]; and zero or negative reliability scores are a good indication of information that can be considered noise.

  12. Another definition of synset is that it is a set of synonyms that are interchangeable without changing the truth value of any propositions in which they are embedded.

  13. Diversity, in EA, is a measure of homogeneity of the individuals in the population. A drop in diversity indicates an increased number of identical individuals, which is not desirable for the progress of evolution.

  14. We define two concepts from different semantic networks as interchangeable if both can replace the other in all, or part, of the relations the other is involved in, queried from commonsense knowledge bases.

  15. We define a distinct concept as attachable to a semantic network if at least one commonsense relation connecting the concept to any of the concepts in the network can be discovered from commonsense knowledge bases.

  16. Kepler argued, in his Astronomia Nova, as light can travel undetectably on its way between the source and destination, and yet illuminate the destination, so can motive force be undetectable on its way from the Sun to planet, yet affect planet’s motion.

  17. Readily available by using WordNet [34].

  18. For ConceptNet version 4: IsA, HasA, PartOf, UsedFor, AtLocation, CapableOf, MadeOf, CreatedBy, HasSubevent, HasFirstSubevent, HasLastSubevent, HasPrerequisite, MotivatedByGoal, Causes, Desires, CausesDesire, HasProperty, ReceivesAction, DefinedAs, SymbolOf, LocatedNear, ObstructedBy, ConceptuallyRelatedTo, InheritsFrom.

References

  1. Balkin JM (1998) Cultural software: a theory of ideology. Yale University Press, New Haven

    Google Scholar 

  2. Bedau MA, Snyder E, Brown CT (1997) A comparison of evolutionary activity in artificial evolving systems and in the biosphere. In: Husbands P, Harvey I (eds) Proceedings of the fourth European conference on artificial life. MIT Press, Cambridge, pp 125–134

  3. Bickhard MH, Campbell DT (2003) Variations in variation and selection: the ubiquity of the variation-and-selective-retention ratchet in emergent organizational complexity. Found Sci 8:215–2182

    Article  Google Scholar 

  4. Boden MA (2009) Computer models of creativity. AI Mag 30(3):23–34

    Google Scholar 

  5. Chafe E (1991) Tonal allegory in the vocal music of JS Bach. University of California Press, Berkeley

    Google Scholar 

  6. Chen D, Aoki T, Homma N, Terasaki T, Higuchi T (2002) Graph-based evolutionary design of arithmetic circuits. IEEE Trans Evolut Comput 6(1):86–100

    Article  Google Scholar 

  7. Clement J (1988) Observed methods for generating analogies in scientific problem solving. Cogn Sci 12:563–586

    Article  Google Scholar 

  8. Colton S, Wiggins G (2012) Computational creativity: the final frontier? In: de Raedt C, Bessiere C, Dubois D, Doherty P (eds) Proceedings of the 20th European conference on artificial intelligence. IOS Press, Amsterdam, pp 21–26

  9. Dawkins R (1976) The selfish gene. Oxford University Press, New York City

    Google Scholar 

  10. Dennett DC (1995) Darwin’s dangerous idea: evolution and the meanings of life. Simon & Schuster, NY

    Google Scholar 

  11. Diochnos DI (2013) Commonsense reasoning and large network analysis: a computational study of ConceptNet 4 http://arxiv.org/abs/1304.5863

  12. Falkenhainer B, Forbus KD, Gentner D (1989) The structure-mapping engine: algorithm and examples. Artif Intell 41:1–63

    Article  MATH  Google Scholar 

  13. Fauconnier G, Mark T (2002) The way we think: conceptual blending and the mind’s hidden complexities. Basic Books, New York

    Google Scholar 

  14. Fellbaum C (1998) WordNet: an electronic lexical database. MIT Press, Cambridge

    MATH  Google Scholar 

  15. French RM (2002) The computational modeling of analogy-making. Trends Cogn Sci 6(5):200–205

    Article  Google Scholar 

  16. Gabora L, Kaufman SB (2010) Evolutionary approaches to creativity. Cambridge University Press, Cambridge

    Google Scholar 

  17. Gentner D (1983) Structure-mapping: a theoretical framework for analogy. Cogn Sci 7:155–170

    Article  Google Scholar 

  18. Gil-White F (2008) Let the meme be (a meme): insisting too much on the genetic analogy will turn it into a straightjacket. In: Botz-Bornstein T (ed) Culture, nature, memes. Cambridge Scholars, Newcastle upon Tyne

    Google Scholar 

  19. Havasi C, Speer R, Alonso J (2007) ConceptNet 3: a flexible, multilingual semantic network for common sense knowledge. In: Proceedings of recent advances in natural language processing

  20. Hofstadter DR (1995) Fluid concepts and creative analogies: computer models of the fundamental mechanisms of thought. Basic Books, New York

    Google Scholar 

  21. Hofstadter DR (2001) Analogy as the core of cognition. In: Gentner D, Holyoak KJ, Kokinov B (eds) Analogical mind: perspectives from cognitive science. MIT Press, Cambridge, pp 499–538

    Google Scholar 

  22. Hofstadter DR, Dennett DC (1981) The mind’s I: fantasies and reflections on self and soul. Basic, New York

    MATH  Google Scholar 

  23. Koza JR, Keane MA, Streeter MJ, Mydlowec W, Yu J, Lanza G (2003) Genetic programming IV: routine human-competitive machine intelligence. Kluwer, Dordrecht

    Google Scholar 

  24. Kuo YL, Hsu J (2010) Bridging common sense knowledge bases with analogy by graph similarity. In: Workshops at the twenty-fourth AAAI conference on artificial intelligence

  25. Larkey LB, Love BC (2003) CAB: connectionist analogy builder. Cogn Sci 27(2003):781–794

    Article  Google Scholar 

  26. Mabu S, Hirasawa K, Hu J (2007) A graph-based evolutionary algorithm: genetic network programming (GNP) and its extension using reinforcement learning. Evol Comput 15(3):369–398

    Article  Google Scholar 

  27. Manurung HM (2003) An evolutionary algorithm approach to poetry generation. Ph.D. thesis, University of Edinburgh, Edinburgh, UK

  28. Martindale C, Locher P, Petrov VM (2007) Evolutionary and neurocognitive approaches to aesthetics, creaticity and the arts. Foundations and frontiers of aesthetics, Baywood Publishing Company

  29. McCarthy J (1958) Programs with common sense. In: Symposium on mechanization of thought processes. National Physical Laboratory, Teddington

  30. Minsky M (2006) Emotion machine: commonsense thinking, artificial intelligence, and the future of the human mind. Simon & Schuster, New York

    Google Scholar 

  31. Montes HA, Wyatt JL (2004) Graph representation for program evolution: an overview. Tech. rep., University of Birmingham School of Computer Science

  32. Moscato PA, Cotta C, Mendes A (2004) Studies in fuzziness and soft computing—new optimization techniques in engineering, chap. memetic algorithms. Springer, New York

    Google Scholar 

  33. Mueller ET (2006) Commonsense reasoning. Morgan Kaufmann, Los Altos

  34. Pedersen T, Patwardhan S, Michelizzi J (2004) Wordnet: similarity: measuring the relatedness of concepts. In: Demonstration papers at HLT-NAACL 2004, pp 38–41. Association for Computational Linguistics

  35. Pereira FB, Machado P, Costa E, Cardoso A (1999) Graph based crossover—a case study with the busy beaver problem. In: Proceedings of the genetic and evolutionary computation conference, vol 2. Morgan Kaufmann, Los Altos, pp 1149–1155

  36. Pereira FC (2007) Creativity and artificial intelligence: a conceptual blending approach. Mouton de Gruyter, New York

    Google Scholar 

  37. Pezzotta E (2012) The metaphor of dance in Stanley Kubrick’s 2001: A space odyssey, a clockwork orange and full metal jacket. J Adapt Film Perform 5(1):51–64

  38. Pohlheim H (2006) Genetic and evolutionary algorithm toolbox documentation http://www.geatbx.com/docu/algindex-02.html

  39. Poli R (1999) New ideas in optimisation, chap. parallel distributed genetic programming. McGraw-Hill, New York

    Google Scholar 

  40. Skusa A, Bedau MA (2002) Towards a comparison of evolutionary creativity in biological and cultural evolution. In: Standish R, Abbass HA, Bedau MA (eds) Artificial life VIII. MIT Press, Cambridge, pp 233–242

    Google Scholar 

  41. Sowa JF (2000) Knowledge representation: logical philosophical, and computational foundations. Brooks/Cole, Pacific Grove

    Google Scholar 

  42. Spears WM (1992) Foundations of genetic algorithms 2, chap. crossover or mutation?. Morgan Kaufmann, Los Altos, pp 221–237

  43. Teller A (1998) Algorithm evolution with internal reinforcement for signal understanding. Ph.D. thesis, Carnegie Mellon University, Pittsburgh, PA

  44. Veale T, Keane M (1997) The competence of sub-optimal structure mapping on hard analogies. In: Proceedings of the 15th international joint conference on AI. Morgan Kauffman, San Mateo

  45. Zhu J, Ontañón S (2013) Evaluating analogy-based story generation: an empirical study. In: Proceedings of the 9th annual AAAI conference on artificial intelligence and interactive digital entertainment (AIIDE 2013). Boston, MA

Download references

Acknowledgments

This work was supported by a JAE-Predoc fellowship from CSIC, and the research grants: 2009-SGR-1434 from the Generalitat de Catalunya, CSD2007-0022 from MICINN, and Next-CBR TIN2009-13692-C03-01 from MICINN. We thank the three anonymous reviewers whose input has considerably improved the article.

Author information

Authors and Affiliations

  1. Department of Computer Science & Hamilton Institute, National University of Ireland Maynooth, Maynooth, Co. Kildare, Ireland

    Atılım Güneş Baydin

  2. Artificial Intelligence Research Institute, IIIA - CSIC, Campus Universitat Autònoma de Barcelona, 08193, Bellaterra, Spain

    Ramon López de Mántaras

  3. Department of Computer Science, Drexel University, 3141 Chestnut Street, Philadelphia, PA, 19104, United States

    Santiago Ontañón

Authors
  1. Atılım Güneş Baydin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ramon López de Mántaras
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Santiago Ontañón
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Atılım Güneş Baydin.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Baydin, A.G., López de Mántaras, R. & Ontañón, S. A semantic network-based evolutionary algorithm for computational creativity. Evol. Intel. 8, 3–21 (2015). https://doi.org/10.1007/s12065-014-0119-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12065-014-0119-1

Keywords

Search

Navigation