Modeling and analysis of genetic algorithm with tournament selection

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

Volumn 1829, Issue , 2000, Pages 84-95

  Eremeev, Anton V ^a  

^a SOBOLEV INSTITUTE OF MATHEMATICS   (Russian Federation)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER SCIENCE; COMPUTERS;

BIT COUNTING; MODEL AND ANALYSIS; SPECIAL STRUCTURE; TOURNAMENT SELECTION; UPPER AND LOWER BOUNDS; VERTEX COVER PROBLEMS;

GENETIC ALGORITHMS;

EID: 84902193800     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/10721187_6     Document Type: Conference Paper

References (13)

1. Bäck, T.: The interaction of mutation rate, selection, and self-adaptation within a genetic algorithm. In: Proc. of Parallel Problem Solving from Nature (PPSN II) Männer, R. and Manderick, B. (eds.). North Holland (1993) 85-94
2. Eremeev, A.V.: A genetic algorithm with a non-binary representation for the set covering problem. In: Proc. of Operations Research (OR'98). Springer-Verlag (1999) 175-181
3. Goldberg, D.E.: Genetic algorithms in search, optimization and machine learning. Addison Wesley Publishing Company (1989)
4. Goldberg, D.E., Korb, B. and Deb, K.: Messy genetic algorithms: motivation, analysis, and first results. Complex Systems, 3 (5) (1989) 493-530
5. Mühlenbein, H.: How genetic algorithms really work: I. Mutation and hillclimbing. In: Proc. of Parallel Problem Solving from Nature (PPSN II) Männer, R. and Manderick, B. (eds.). North Holland (1993) 15-26
6. Mühlenbein, H., Mahnig, T. and Rodriguez, A. O.: Schemata, distributions and graphical models in evolutionary optimization. To Appear in Journal of Heuristics.
7. Mühlenbein, H. and Schlierkamp-Voosen, D.: The science of breeding and its application to the breeder genetic algorithm (BGA). Evolutionary Computation, 1 (1) (1993) 335-360
8. Nix, A. and Vose, M. D.: Modeling genetic algorithms with Markov chains. Annals of Mathematics and Artificial Intelligence, 5 (1992) 79-88
9. Rudolph, G.: Convergence analysis of canonical genetic algorithms. IEEE Transactions on Neural Networks, 5 (1) (1994) 96-101
10. Thierens, D. and Goldberg, D.E.: Convergence models of genetic algorithm selection schemes In: Proc. of Parallel Problem Solving from Nature (PPSN III) Davidor, Y., Schwefel, H.- P. and Männer, R. (eds.). Springer-Verlag (1994) 117-129
11. Vose, M. D.: Modeling simple genetic algorithms. Evolutionary Computation, 3 (4) (1995) 453-472
12. Vose, M. D. and Wright, A.H.: The Walsh transform and the theory of the simple genetic algorithm. Genetic Algorithms for Pattern Recognition. Pal, S. K., Wang, P. P. (eds.) (1995) 25-44
13. Zaozerskaya, L.A.: Investigation and solving of some classes of integer programming problems on the basis of the regular partitions. PhD. thesis, Omsk Branch of Sobolev Institute of Mathematics, SB RAS. (1998)