Generalized schemata theorem incorporating twin removal for protein structure prediction

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4774 LNBI, Issue , 2007, Pages 84-97

  Hoque, Md Tamjidul ^a   Chetty, Madhu ^a   Dooley, Laurence S ^a  

^a UNIVERSITY OF BALLARAT   (Australia)

Author keywords

Hard optimization problem; Protein structure prediction; Schemata theorem; Similarity in population; Twin removal

Indexed keywords

CONVERGENCE OF NUMERICAL METHODS; GENETIC ALGORITHMS; OPTIMIZATION; PROBLEM SOLVING; THEOREM PROVING;

PROTEIN STRUCTURES; SCHEMATA THEOREMS; SEARCH LANDSCAPES;

PROTEINS;

EID: 38349063423     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-75286-8_9     Document Type: Conference Paper

References (44)

1. Berger, B., Leighton, T.: Protein Folding in the Hydrophobic-Hydrophilic (HP) Model is NP-Complete. Journal of Computational Biology 5, 27 (1998)
2. Crescenzi, P., Goldman, D., Papadimitriou, C., Piccolboni, A., Yannakakis, M.: On the complexity of protein folding (extended abstract), p. 597. ACM, New York (1998)
3. Chen, M., Lin, K.Y.: Universal amplitude ratios for three-dimensional self-avoiding walks. Journal of Physics A: Mathematical and General 35, 1501 (2002)
4. Guttmann, A.J.: Self-avoiding walks in constrained and random geometries. Elsevier, Amsterdam (2005)
5. Jiang, T., Cui, Q., Shi, G., Ma, S.: Protein folding simulation of the hydrophobichydrophilic model by computing tabu search with genetic algorithms. In: ISMB (2003)
6. König, R., Dandekar, T.: Refined Genetic Algorithm Simulation to Model Proteins. Journal of Molecular Modeling 5 (1999)
7. Lamont, G.B., Merkie, L.D.: Toward effective polypeptide chain prediction with parallel fast messy genetic algorithms. In: Fogel, G., Corne, D. (eds.) Evolutionary Computation in Bioinformatics, p. 137 (2004)
8. Pedersen, J.T., Moult, J.: Ab initio protein folding simulations with genetic algorithms: simulations on the complete sequence of small proteins. Proteins 29, 179 (1997)
9. Schulze-Kremer, S.: Genetic Algorithms and Protein Folding, vol. 1996 (2007)
10. Takahashi, O., Kita, H., Kobayashi, S.: Protein Folding by A Hierarchical Genetic Algorithm. In: 4th Int. Symp. AROB (1999)
11. Unger, R., Moult, J.: On the Applicability of Genetic Algorithms to Protein Folding. In: The Twenty-Sixth Hawaii International Conference on System Sciences, p. 715 (1993)
12. Unger, R., Moult, J.: Genetic Algorithms for Protein Folding Simulations. Journal of Molecular Biology 231, 75 (1993)
13. Hoque, M.T., Chetty, M., Dooley, L.S.: Significance of Hybrid Evolutionary Computation for Ab Inito Protein Folding Prediction. Springer, Heidelberg (2006)
14. Hoque, M.T., Chetty, M., Dooley, L.S.: A New Guided Genetic Algorithm for 2D Hydrophobic-Hydrophilic Model to Predict Protein Folding. In: IEEE CEC IEEE Computer Society Press, Los Alamitos (2005)
15. Bastolla, U., Frauenkron, H., Gerstner, E., Grassberger, P., Nadler, W.: Testing a new Monte Carlo Algorithm for Protein Folding. Nat. Center for Biotech. Info. 32, 52 (1998)
16. Liang, F., Wong, W.H.: Evolutionary Monte Carlo for protein folding simulations. J. Chem. Phys. 115 (2001)
17. Shmygelska, A., Hoos, H.H.: An ant colony optimization algorithm for the 2D and 3D hydrophobic polar protein folding problem, BMC Bioinformatics 6 (2005)
18. Fogel, D.B.: EVOLUTIONARY COMPUTATION Towards a new philosophy of Machine Intelligence. IEEE Press, Los Alamitos (2000)
19. Sareni, B., Krähenbühl, L., Nicolas, A.: Effective Genetic Algorithms for Solving Hard Constrained Optimization Problems. IEEE Transaction on Magetics 36 (2000)
20. Rudolph, G.: Convergence analysis of canonical genetic algorithms. ITNN 5, 96 (1994)
21. Altenberg, L.: The Schema Theorem and Price's Theorem Foundations of Genetic Algorithms 3 (1995)
22. Fogel, D.B., Ghozeil, A.: Schema processing, proportional selection, and the misallocation of trials in genetic algorithms. Information Science 122, 93 (2000)
23. Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution (1992)
24. Whitley, D.: An Overview of Evolutionary Algorithms. Journal of Information and Software Technology 43, 817 (2001)
25. Ronald, S.: Duplicate Genotypes in a Genetic algorithm. In: IEEE WCCI, p. 793. IEEE Computer Society Press, Los Alamitos (1998)
26. Haupt, R.L., Haupt, S.E.: Practical Genetic Algorithms (2004)
27. Hoque, M.T., Chetty, M., Dooley, L.S.: Critical Analysis of the Schemata Theorem: The Impact of Twins and the Effect in the Prediction of Protein Folding using Lattice Model, GSIT, MONASH University, TR-2005/8 (2005)
28. Deb, K., Goldberg, D.E.: An investigation of niche and species formation in genetic function optimization. In: 3rd Int. Conf. on Genetic Algorithms, p. 42 (1989)
29. Coello, C.A.C.: An Updated Survey of GA-Based Multiobjective Optimization Techniques. ACM Computing Surveys 32, 109 (2000)
30. Rogers, A., Prügle-Bennett, A.: Genetic Drift in Genetic Algorithm Selection Schemes. IEEE Transaction on Evolutionary Computation 3, 298 (1999)
31. Eshelman, L.J., Schaffer, J.D.: Preventing premature convergence in genetic algorithms by preventing incast. In: 4th Int. Conf. on Genetic Algorithms, p. 115 (1991)
32. Hoque, M.T., Chetty, M., Dooley, L.S.: Non-Isomorphic Coding in Lattice Model and its Impact for Protein Folding Prediction Using Genetic Algorithm. In: IEEE Computational Intelligence in Bioinformatics and Computational Biology IEEE CIBCB, Canada (2006)
33. Toma, L., Toma, S.: Contact interactions methods: A new Algorithm for Protein Folding Simulations. Protein Science 5, 147 (1996)
34. Lesh, N., Mitzenmacher, M., Whitesides, S.: A Complete and Effective Move Set for Simplified Protein Folding. In: RECOMB (2003)
35. Bornberg-Bauer, E.: Chain Growth Algorithms for HP-Type Lattice Proteins. In: RECOMB'97 (1997)
36. Backofen, R., Will, S.: A Constraint-Based Approach to Fast and Exact Structure Prediction in Three-Dimensional Protein Models. Kluwer Academic Publishers, Dordrecht (2005)
37. Dill, K.A.: Theory for the Folding and Stability of Globular Proteins. Bio-chemistry 24, 501 (1985)
38. Hart, W.E., Istrail, S.: HP Benchmarks (2005), http://www.cs.sandia.gov/
39. Vose, M.D.: The Simple Genetic Algorithm. MIT Press, Cambridge (1999)
40. Holland, J.H.: Adaptation in Natural And Artificial Systems. MIT Press, Cambridge (2001)
41. Goldberg, D.E.: Genetic Algorithm Search, Optimization, and Machine Learning. Addison-Wesley Publishing Company, Reading (1989)
42. Davis, L.: Handbook of Genetic Algorithm. VNR, New York (1991)
43. PDB, Protein Data Base, vol. 2007 (2007), http://www.rcsb.org/pdb/
44. Digalakis, J.G., Margaritis, K.G.: An experimental Study of Benchmarking Functions for Genetic Algorithms. Intern. J. Computer Math. 79, 403 (2002)