Source: J. Appl. Probab. Volume 42, Number 1
(2005), 39-51.
We consider two versions of a simple evolutionary algorithm (EA)
model for protein folding at zero temperature, namely the
(1 + 1)-EA on the LeadingOnes problem. In this schematic model,
the structure of the protein, which is encoded as a bit-string of
length n, is evolved to its native conformation through a
stochastic pathway of sequential contact bindings. We study the
asymptotic behavior of the hitting time, in the mean case
scenario, under two different mutations: the one-flip,
which flips a unique bit chosen uniformly at random in the
bit-string, and the Bernoulli-flip, which flips each bit in
the bit-string independently with probability c/n,
for some c ∈ R+
(0 ≤ c ≤ n). For each algorithm, we
prove a law of large numbers, a central limit theorem, and compare
the performance of the two models.
References
Anfinsen, C. B. (1973). Principles that govern the folding of a protein chain (Nobel lecture). Science 191, 223--230.
Bäck, T. (1993). Optimal mutation rates in genetic search. In Proc. 5th Internat. Conf. Genet. Algorithms, ed. S. Forrest, Morgan Kaufmann, San Fransisco, pp. 2--8.
Bakk, A. et al. (2000). Pathways in two-state protein folding. Biophys. J. 79, 2722--2727.
Bérard, J. and Bienvenüe, A. (2000). Convergence of a genetic algorithm with finite population. In Mathematics and Computer Science (Versailles, 2000), Birkhäuser, Basel, pp. 155--163.
Bérard, J. and Bienvenüe, A. (2003). Sharp asymptotic results for simplified mutation-selection algorithms. Ann. Appl. Prob. 13, 1534--1568.
Binder, K. (1987). Applications of the Monte Carlo Method in Statistical Physics. Springer, Berlin.
Mathematical Reviews (MathSciNet):
MR555878
Cerf, R. (1996). The dynamics of mutation-selection algorithms with large population sizes. Ann. Inst. H. Poincaré Prob. Statist. 32, 455--508.
Cerf, R. (1998). Asymptotic convergence of genetic algorithms. Adv. Appl. Prob. 30, 521--550.
Del Moral, P. and Guionnet, A. (2001). On the stability of interacting processes with applications to filtering and genetic algorithms. Ann. Inst. H. Poincaré Prob. Statist. 37, 155--194.
Dill, K. A., Feibig, K. M. and Chan, H. S. (1993). Cooperativity in protein folding kinetics. Proc. Nat. Acad. Sci. USA 90, 1942--1946.
Droste, S., Jansen, T. and Wegener, I. (1998). A rigorous complexity analysis of the $(1+1)$ evolutionary algorithm for linear functions with boolean inputs. In Proc. IEEE Internat. Conf. Evolutionary Comput. (ICEC'98). IEEE Press, Piscataway, NJ, pp. 499--504.
Droste, S., Jansen, T. and Wegener, I. (2002). On the analysis of the $(1+1)$ evolutionary algorithm. Theoret. Comput. Sci. 276, 51--81.
Garnier, J., Kallel, L. and Schoenauer, M. (1999). Rigorous hitting times for binary mutations. Evolutionary Comput. 7, 173--203.
Jansen, T. and Wegener, I. (2001). How to cope wih plateaus of constant fitness and when to reject strings of the same fitness. IEEE Trans. Evolutionary Comput. 5, 589--599.
Levinthal, C. (1968). Are there pathways for protein folding? J. Chim. Phys. Phys.-Chim. Biol. 65, 44--45.
Mazza, C. and Piau, D. (2001). On the effect of selection in genetic algorithms. Random Structures Algorithms 18, 185--200.
Mühlenbein, H. (1992). How genetic algorithms really work: I. Mutation and hill-climbing. In Parallel Problem Solving from Nature PPSN II, eds R. Männer and R. Manderick, North-Holland, Amsterdam, pp. 15--25.
Rabinovich, Y. and Wigderson, A. (1999). Techniques for bounding the convergence rate of genetic algorithms. Random Structures Algorithms 14, 111--138.
Rudolph, G. (1997). Convergence Properties of Evolutionary Algorithms. Kovac, Hamburg.
Schellman, J. A. (1958). The factors affecting the stability of hydrogen-bounded polypeptide structures in solution. J. Phys. Chem. 62, 1485--1494.