Skip to main content
SpringerLink
Log in

Identification and Exploitation of Linkage by Means of Alternative Splicing

  • Chapter
Linkage in Evolutionary Computation

Part of the book series: Studies in Computational Intelligence ((SCI,volume 157))

  • 466 Accesses

Summary

Alternative splicing is an important cellular process that allows the expression of a large number of unique cell-specific proteins from the same underlying strand of DNA, and thereby drastically increases the organism’s phenotypic plasticity. Its emergence is facilitated by the modular composition of genes into numerous semi-autonomous building blocks. In artificial evolution, such modular composition is usually unknown initially, but once learned may greatly increase the algorithm’s efficiency.

In this paper, an abstract interpretation of alternative splicing is presented that emulates some of the properties of its natural counterpart. Two appoaches, both based upon a simple (1+1) evolutionary algorithm, are described and shown to work well on established benchmark problems. The first algorithm, eAS, is designed for cyclical dynamic optimisation problems: it systematically merges the problem variables into groups that capture the properties exhibited by a finite number of successive states and reuses that information when required. The second algorithm, iAS, employs a systematic search to identify a sub-set of variables for which simultaneous inversion affords an increase in fitness. This approach seems particularly useful for problems that have many local optima that are far apart in the search space. Results from a systematic series of experiments highlight the intrinsic attributes of each algorithm, and allow analysis in terms of the identification and exploitation of linkage.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Altenberg, L.: NK fitness landscapes. In: Bäck, T., Fogel, D.B., Michalewicz, Z. (eds.) The Handbook of Evolutionary Computation, pp. B2.7:2–B2.7:10. Oxford University Press, Oxford (1997)

    Google Scholar 

  2. Baker, B.S.: Sex in flies: The splice of life. Nature 340, 521–524 (1989)

    Article  Google Scholar 

  3. Black, D.L.: Mechanisms of alternative pre-messenger RNA splicing. Annual Review of Biochemistry 72, 291–336 (2003)

    Article  Google Scholar 

  4. Bosman, P.A.N., Poutrè, H.L.: Learning and anticipation in online dynamic optimization with evolutionary algorithms: the stochastic case. In: Proceedings of the 2007 Genetic and Evolutionary Computation Conference, pp. 1165–1172 (2007)

    Google Scholar 

  5. Branke, J.: Evolutionary Optimization in Dynamic Environments. Kluwer, Dordrecht (2001)

    Google Scholar 

  6. Branke, J., Kaußler, T., Schmidt, C., Schmeck, H.: A multi-population approach to dynamic optimization problems. In: Parmee, I.C. (ed.) Adaptive Computing in Design and Manufacture 2000, pp. 299–308. Springer, Heidelberg (2000)

    Google Scholar 

  7. Cobb, H.G.: An investigation into the use of hypermutation as an adaptive operator in genetic algorithms having continuous, time-dependant nonstationary environments. Technical report, Naval Research Laboratory, Washington, USA (1990)

    Google Scholar 

  8. Collard, P., Aurand, J.-P.: DGA: An efficient genetic algorithm. In: Proceedings of the Eleventh European Conference on Artificial Intelligence, pp. 487–491 (1994)

    Google Scholar 

  9. Dasgupta, D., McGregor, D.R.: Nonstationary function optimization using the structured genetic algorithm. In: Männer, R., Manderick, B. (eds.) Parallel Problem Solving from Nature, vol. 2, pp. 145–154. Elsevier, Amsterdam (1992)

    Google Scholar 

  10. Di Paolo, E.A.: Rhythmic and non-rhythmic attractors in asynchronous random boolean networks. BioSystems 59, 185–195 (2001)

    Article  Google Scholar 

  11. Fisher, R.A.: The Genetical Theory of Natural Selection. Clarendon Press, Oxford (1930)

    MATH  Google Scholar 

  12. Gaspar, A., Clergue, M., Collard, P.: Folding genetic algorithms: the royal road toward an optimal chromosome’s expressiveness. In: Second International ISCS Symposium on Soft Computing (1997)

    Google Scholar 

  13. Gaspar, A., Collard, P.: Time dependent optimization with a folding genetic algorithm. In: IEEE International Conference on Tools for Artificial Intelligence, pp. 207–214. IEEE Computer Society Press, Los Alamitos (1997)

    Google Scholar 

  14. Goldberg, D.E.: The Design of Innovation: Lessons from and for competent genetic algorithms. Kluwer Academic Publishers, Dordrecht (2002)

    MATH  Google Scholar 

  15. Goldberg, D.E., Korb, D.E., Deb, K.: Messy genetic algorithms: Motivation, analysis and first results. Complex Systems 3, 493–530 (1989)

    MATH  MathSciNet  Google Scholar 

  16. Goldberg, D.E., Smith, R.E.: Nonstationary function optimization using genetic algorithms with dominance and diploidy. In: Grefenstette, J.J. (ed.) Second International Conference on Genetic Algorithms, pp. 59–68. Lawrence Erlbaum Associates (1987)

    Google Scholar 

  17. Goldberg, D.E., Deb, K., Kargupta, H., Harik, G.: Rapid, accurate optimization of difficult problems using fast messy genetic algorithms. In: Proceedings of the Fifth International Conference on Genetic Algorithms, pp. 56–64 (1993)

    Google Scholar 

  18. Graveley, B.R.: Alternative splicing: increasing diversity in the proteomic world. TRENDS in Genetics 17(2), 100–107 (2001)

    Article  Google Scholar 

  19. Grefenstette, J.J.: Genetic algorithms for changing environments. In: Manner, R., Manderick, B. (eds.) Proceedings of the Second International Conference on Parallel Problem Solving from Nature, vol. 2, pp. 137–144. Elsevier, Amsterdam (1992)

    Google Scholar 

  20. Hadad, B.S., Eick, C.F.: Supporting polyploidy in genetic algorithms using dominance vectors. In: Angeline, P.J., McDonnell, J.R., Reynolds, R.G., Eberhart, R. (eds.) EP 1997. LNCS, vol. 1213, pp. 223–234. Springer, Heidelberg (1997)

    Chapter  Google Scholar 

  21. Harik, G.: Learning Gene Linkage to Efficiently solve problems of bounded difficulty using genetic algorithms. PhD thesis, University of Michigan, Ann Arbor (1997)

    Google Scholar 

  22. Harik, G.R.: Linkage learning via probabilistic modeling in the ECGA. Technical Report 99010, University of Illinois at Urbana-Champain (1999)

    Google Scholar 

  23. Harik, G.R., Lobo, F.G., Goldberg, D.E.: The compact genetic algorithm. IEEE 3(4), 287 (1999)

    Google Scholar 

  24. Harrington, E.D., Boue, S., Valcarcel, J., Reich, J.G., Bork, P.: Estimating rates of alternative splicing in mammals and invertebrates. Nature Genetics 36(9), 915–917 (2004)

    Article  Google Scholar 

  25. Herbet, A., Rich, A.: RNA processing and the evolution of eukaryotes. Nature Genetics 21, 265–269 (1999)

    Article  Google Scholar 

  26. Huang, C.-F., Rocha, L.M.: Exploration of RNA editing and design of robust genetic algorithms. In: Proceedings of the 2003 IEEE Congress on Evolutionary Computation. IEEE Press, Los Alamitos (2003)

    Google Scholar 

  27. International Human Genome Sequencing Consortium. Initial sequencing and analysis of the human genome. Nature, 409, 860–921 (2001)

    Google Scholar 

  28. Kargupta, H., Gosh, S.: Towards machine learning through genetic code-like transformations. Genetic Programming and Evolvable Machine Journal 3(3), 231–258 (2002)

    Article  MATH  Google Scholar 

  29. Karuptga, H.: The gene expression messy genetic algorithm. In: Proceedings of the IEEE International Conference on Evolutionary Computation (1996)

    Google Scholar 

  30. Kauffman, S.A.: The Origins of Order. Oxford University Press, Oxford (1993)

    Google Scholar 

  31. Kondrashov, F.A., Koonin, E.V.: Origin of alternative splicing by tandem exon duplicaton. Human Molecular Genetics 10(23), 2661–2669 (2001)

    Article  Google Scholar 

  32. Ladd, A.N., Cooper, T.A.: Finding signals that regulate alternative splicing in the post-genomic era. Genome Biology 3(11), 1–16 (2002)

    Article  Google Scholar 

  33. Levenick, J.R.: Swappers: Introns promote flexibility, diversity and invention. In: Orlando, F.L., Banzhaf, W., Daida, J., Eiben, A.E., Garzon, M.H., Jakiela, H., Smith, R.E. (eds.) Proceeding of Genetic and Evolutionary Computation Conference 1999, vol. 1, pp. 361–368. Morgan Kaufmann, San Francisco (1999)

    Google Scholar 

  34. Lopez, A.J.: Alternative splicing of pre-mRNA: Developmental consequences and mechanisms of regulation. Annual Review of Genetics 32, 279–305 (1998)

    Article  Google Scholar 

  35. Modrek, B., Lee, C.J.: Alternative splicing in the human, mouse and rat genomes is associated with an increased frequency of exon creation and/or loss. Nature Genetics 34(2), 177–180 (2003)

    Article  Google Scholar 

  36. Morrison, R.W.: Designing Evolutionary Algorithms for Dynamic Environments. Springer, Berlin (2004)

    MATH  Google Scholar 

  37. Rohlfshagen, P., Bullinaria, J.A.: Alternative splicing in evolutionary computation: Adaptation in dynamic environments. In: Proceedings of the 2006 IEEE Congress on Evolutionary Computing, pp. 8041–8048. IEEE, Piscataway (2006)

    Google Scholar 

  38. Rohlfshagen, P., Bullinaria, J.A.: Implicit alternative splicing for genetic algorithms. In: Proceedings of the 2007 IEEE Congress on Evolutionary Computing, pp. 47–54. IEEE Press, Los Alamitos (2007)

    Chapter  Google Scholar 

  39. Rohlfshagen, P., Di Paolo, E.A.: The circular topology of rhythm in asynchronous random boolean networks. BioSystems 73, 141–152 (2004)

    Article  Google Scholar 

  40. Yang, S.: Non-stationary problem optimization using the primal-dual genetic algorithm. In: Sarker, R., Reynolds, R., Abbass, H., Tan, K.-C., McKay, R., Essam, D., Gedeon, T. (eds.) Proceedings of the 2003 IEEE Congress on Evolutionary Computation, vol. 3, pp. 2246–2253 (2003)

    Google Scholar 

  41. Yang, S.: PDGA: the primal-dual genetic algorithm. In: Abraham, A., Koppen, M., Franke, K. (eds.) Design and Application of Hybrid Intelligent Systems, pp. 214–223. IOS Press, Amsterdam (2003)

    Google Scholar 

  42. Yang, S.: A comparative study of immune system based genetic algorithms in dynamic environments. In: Proceedings of the 8th annual conference on Genetic and evolutionary computation, pp. 1377–1384 (2006)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom

    Philipp Rohlfshagen & John A. Bullinaria

Authors
  1. Philipp Rohlfshagen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. John A. Bullinaria
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Ying-ping Chen Meng-Hiot Lim

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Rohlfshagen, P., Bullinaria, J.A. (2008). Identification and Exploitation of Linkage by Means of Alternative Splicing. In: Chen, Yp., Lim, MH. (eds) Linkage in Evolutionary Computation. Studies in Computational Intelligence, vol 157. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85068-7_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-85068-7_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-85067-0

  • Online ISBN: 978-3-540-85068-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation