An adaptive genetic algorithm for the minimal switching graph problem

Lecture Notes in Computer Science

Volumn 3448, Issue , 2005, Pages 224-233

  Tang, Maolin ^a  

^a QUEENSLAND UNIVERSITY OF TECHNOLOGY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

COMBINATORIAL MATHEMATICS; COMPUTATIONAL COMPLEXITY; GENETIC ALGORITHMS; GRAPH THEORY; INTEGRATED CIRCUIT LAYOUT; OPTIMIZATION;

COMBINATORIAL OPTIMIZATION; MINIMAL SWITCHING GRAPH (MSG); PERMUTATIONS; SELF-ADAPTIVE ENCODING;

PROBLEM SOLVING;

EID: 24644502254     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-31996-2_21     Document Type: Conference Paper

References (15)

1. Tang, M., Eshraghian, K., and Cheung, H.N.: An Efficient Approach to Constrained Via Minimization for Two-Layer VLSI Routing. Proc. IEEE Asia and South Pacific Design Automation Conference. Hong Kong (1999) 149-152
2. Naclerio, N.J., Masuda, S., and Nakajima, K.: The Via Minimization problem is NP-complete. IEEE Transactions on Computers. 38 (1989) 1604-1608
3. Tang, M., Eshraghian, K., and Cheung, H.N.: A Genetic Algorithm for Constrained Via Minimization. Proc. IEEE International Conference on Neural Information Processing. Perth (1999) 435-440
4. Harik, G.R. and Goldberg, D.E.: Linkage Learning. IlliGAL Technical Report 96006 (1996)
5. Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)
6. Goldberg, D.E., Korb, B.,and Deb, K.: Messy genetic algorithms: Motivation, analysis, and first results. Complex Systems 3 (1989) 493-530
7. Harik, G.R.: Lingake Learning Via Probabilistic Modeling. IlliGAL Technical Report 99010 (1999)
8. Mühlenbein, H. and Paaß, G.: From Recombination of Genes to the Estimation of Distributions I. Binary Parameters. In: Eiben, A., Back, T., Shoenauer, M. and Schwefel, H.-P. (eds): Parallel Problem Solving from Nature - PPSN IV, Springer-Verlag, Berlin (1996) 178-187
9. Harik, G. R., Lobo, F.G. and Goldberg, D.E.: The Compact Genetic Algorithm. International Conference on Evolutionary Computation, IEEE, New Jersey (1998) 523-528.
10. Pelikan, M., Goldberg D.E. and Cantu-Paz, K.: Linkage Problem, Distribution Estimation, and Bayesian Networks. Evolutionary Computation. 8 311-340
11. Sastry, K.: Analysis of Mixing in Genetic Algorithms: A survey. IlliGAL Report No. 2002012 (2002)
12. Sastry, K. and Goldberg D.E.: How Well Does A Single-Point Crossover Mix Building Blocks with Tight Linkage?. IlliGAL Report No. 2002013 (2002)
13. Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. 3rd edn. Springer-Verlag, Berlin Heidelberg New York (1996)
14. Prugel-Bennett, A.: Modeling Cross-Induced Linkage in Genetic Algorithms. IEEE Trans. on Evolutionary Computation 5 (2001) 376-387
15. Thierens, D. and Goldberg, D.E.: Mixing in Genetic Algorithms. Proc. the 5th Int. Conf. on Genetic Algorithms. (1993) 38-45