Summary
In this chapter we extend our previous studies on the self-adaptation of local searchers within a Memetic Algorithm. Self-adaptation allows the MA to learn which local searcher to use during search. In particular, we extend our results in tikya[12], where memes were instantiated as Fuzzy-Logic based local searchers, and we show that our Multimeme algorithms are capable of producing new optimum solutions to instances of the Protein Structure Prediction Problem in the HP-model.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
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.
References
J. Atkins and W. E. Hart. On the intractability of protein folding with a finite alphabet. Algorithmica, pages 279–294, 1999.
B. Berger and T. Leight. Protein folding in the hydrophobic-hydrophilic (HP) model is NP-complete. In Proceedings of The Second Annual International Conference on Computational Molecular Biology, RECOMB 98, pages 30–39. ACM Press, 1998.
L. T. Biegler, T. F. Coleman, A. R. Conn, and F. N. Santosa, editors. Large-Scale optimization with applications. Part III: Molecular structure and optimization, volume 94 of The IMA Volumes in Mathematics and its Applications. Springer-Verlag, New York, 1997.
A. Blanco, D. Pelta, and J. Verdegay. A fuzzy valuation-based local search framework for combinatorial problems. Journal of Fuzzy Optimization and Decision Making, 1(2):177–193, 2002.
B. Carr, W.E. Hart, N. Krasnogor, E. Burke, J. Hirst, and J. Smith. Alignment of protein structures with a memetic evolutionary algorithm. In GECCO-2002: Proceedings of the Genetic and Evolutionary Computation Conference. Morgan Kaufman, 2002.
T. E. Creighton, editor. Protein Folding. W. H. Freeman and Company, 1993.
P. Crescenzi, D. Goldman, C. Papadimitriou, A. Piccolboni, and M. Yannakakis. On the complexity of protein folding. In Proceedings of The Second Annual International Conference on Computational Molecular Biology, RECOMB 98, pages 51–62. ACM Press, 1998.
K. A. Dill. Theory for the folding and stability of globular proteins. Biochemistry, 24:1501, 1985.
G. Greenwood, B. Lee, J. Shin, and G. Fogel. A survey of recent work on evolutionary approaches to the protein folding problem. In Proceedings of the Congress of Evolutionary Computation (GEC), pages 488–495. IEEE, 1999.
M. Khimasia and P. Coveney. Protein structure prediction as a hard optimization problem: The genetic algorithm approach. In Molecular Simulation, volume 19, pages 205–226, 1997.
N. Krasnogor. Studies on the Theory and Design Space of Memetic Algorithms. Ph.D. Thesis, University of the West of England, Bristol, United Kingdom. (http://dirac.chem.nott.ac.uk/~natk/Public/papers.html), 2002.
N. Krasnogor, B. Blackburne, E. Burke, and J. Hirst. Multimeme algorithms for protein structure prediction. In Proceedings of the Parallel Problem Solving from Nature VII. Lecture notes in computer science, 2002.
N. Krasnogor, W.E. Hart, J. Smith, and D. Pelta. Protein structure prediction with evolutionary algorithms. In W. Banzhaf, J. Daida, A. Eiben, M. Garzon, V. Honavar, M. Jakaiela, and R. Smith, editors, GECCO-99: Proceedings of the Genetic and Evolutionary Computation Conference. Morgan Kaufman, 1999.
N. Krasnogor and D. Pelta. Fuzzy memes in multimeme algorithms: a fuzzy-evolutionary hybrid. In J. Verdegay, editor, Fuzzy Sets based Heuristics for Optimization, Studies in Fuzziness and Soft Computing, pages 49–66. Physica Verlag, 2003.
N. Krasnogor, D. Pelta, P. M. Lopez, P. Mocciola, and E. de la Canal. Genetic algorithms for the protein folding problem: A critical view. In C. F. E. Alpaydin, editor, Proceedings of Engineering of Intelligent Systems. ICSC Academic Press, 1998.
N. Krasnogor, D. Pelta, D. H. Marcos, and W. A. Risi. Protein structure prediction as a complex adaptive system. In Proceedings of Frontiers in Evolutionary Algorithms 1998, 1998.
N. Krasnogor and J. Smith. A memetic algorithm with self-adaptive local search: TSP as a case study. In Proceedings of the 2000 Genetic and Evolutionary Computation Conference. Morgan Kaufmann, 2000.
N. Krasnogor and J. Smith. Emergence of profitable search strategies based on a simple inheritance mechanism. In Proceedings of the 2001 Genetic and Evolutionary Computation Conference. Morgan Kaufmann, 2001.
N. Krasnogor and J. Smith. Memetic algorithms: Syntactic model and taxonomy. 2001. submitted to The Journal of Heuristics. Available from the authors.
F. Liang and W. Wong. Evolutionary monte carlo for protein folding simulations. Journal of Chemical Physics, 115(7):3374–3380, 2001.
P. M. Pardalos, D. Shalloway, and G. L. Xue, editors. Global minimization of nonconvex energy functions: Molecular conformation and protein folding, volume 23 of DIM ACS Series in Discrete Mathematics and Theoretical Computer Science. American Mathematical Society, Providence, Rhode Island, 1996.
A. L. Patton. A standard ga approach to native protein conformation prediction. In Proceedings of the Sixth International Conference on Genetic Algorithms, pages 574–581. Morgan Kauffman, 1995.
D. Pelta, A. Blanco, and J. L. Verdegay. A fuzzy adaptive neighborhood search for function optimization. In Fourth International Conference on Knowledge-Based Intelligent Engineering Systems & Allied Technologies, KES 2000, volume 2, pages 594–597, 2000.
D. Pelta, A. Blanco, and J. L. Verdegay. Applying a fuzzy sets-based heuristic for the protein structure prediction problem. Internation journal of Intelligent Systems, 17(7):629–643, 2002.
D. Pelta, A. Blanco, and J. L. Verdegay. Fuzzy adaptive neighborhood search: Examples of application. In J. L. Verdegay, editor, Fuzzy Sets based Heuristics for Optimization, Studies in Fuzziness and Soft Computing, pages 1–20. Physica-Verlag, 2003.
D. Pelta, N. Krasnogor, A. Blanco, and J. L. Verdegay. F.a.n.s. for the protein folding problem: Comparing encodings and search modes. In Fourth International Metaheuristics Conference, MIC 2001, 2001.
A. Piccolboni and G. Mauri. Protein structure prediction as a hard optimization problem: The genetic algorithm approach. In N. e. a. Kasabov, editor, Proceedings of ICONIP’ 97. Springer, 1998.
A. A. Rabow and H. A. Scheraga. Improved genetic algorithm for the protein folding problem by use of a cartesian combination operator. Protein Science, 5:1800–1815, 1996.
S.-K. S. Genetic algorithms for protein tertiary structure prediction. In Parallel Problem Solving from Nature-PPSN II. North-Holland, 1992.
R. Unger and J. Moult. A genetic algorithm for three dimensional protein folding simulations. In Proceedings of the 5th International Conference on Genetic Algorithms (ICGA-93), pages 581–588. Morgan Kaufmann, 1993.
R. Unger and J. Moult. Genetic algorithms for protein folding simulations. Journal of Molecular Biology, 231(1):75–81, 1993.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Pelta, D.A., Krasnogor, N. (2005). Multimeme Algorithms Using Fuzzy Logic Based Memes For Protein Structure Prediction. In: Hart, W.E., Smith, J.E., Krasnogor, N. (eds) Recent Advances in Memetic Algorithms. Studies in Fuzziness and Soft Computing, vol 166. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32363-5_3
Download citation
DOI: https://doi.org/10.1007/3-540-32363-5_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22904-9
Online ISBN: 978-3-540-32363-1
eBook Packages: EngineeringEngineering (R0)