Running time analysis of a multiobjective evolutionary algorithm on simple and hard problems

Lecture Notes in Computer Science

Volumn 3469, Issue , 2005, Pages 112-131

  Kumar, Rajeev ^a   Banerjee, Nilanjan ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY   (India)

Author keywords

[No Author keywords available]

Indexed keywords

BOOLEAN FUNCTIONS; LARGE SCALE SYSTEMS; POPULATION STATISTICS; PROBLEM SOLVING;

QUADRATIC FUNCTIONS; REAL-WORLD APPLICATIONS; RESTRICTED MATING POOL (REMO); RUNNING TIME ANALYSIS;

GENETIC ALGORITHMS;

EID: 24944565418     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/11513575_7     Document Type: Conference Paper

References (38)

1. Garey, M.R., Johnson, D.S.: Computers and Interactability: A Guide to the Theory of NP-Completeness. San Francisco, LA: Freeman (1979)
2. Hochbaum, D.: Approximation Algorithms for NP-Hard Problems. Boston, MA: PWS (1997)
3. Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Trans. Evolutionary Computation 3 (1999) 251-211
4. Knowles, J.D., Corne, D.W.: A Comparison of Encodings and Algorithms for Multiobjective Minimum Spanning Tree Problems. In: Proc. Congress on Evolutionary Computation (CEC-01). Volume 1. (2001) 544-551
5. Kumar, R., Singh, P.K., Chakrabarti, P.P.: Improved quality of solutions for multiobjective spanning tree problem using evolutionary algorithm. In: Proc. Int. Conf. High Performance Computing (HiPC-04), LNCS 3296. (2004) 494-503
6. Kumar, R., Rockett, P.I.: Multiobjective genetic algorithm partitioning for hierarchical learning of high-dimensional pattern spaces: A learning-follows-decomposition strategy. IEEE Trans. Neural Networks 9 (1998) 822-830
7. Kumar, R.: Codebook design for vector quantization using multiobjective genetic algorithms. In: Proc. PPSN/SAB Workshop on Multiobjective Problem Solving from Nature. (2000)
8. Kumar, R., Parida, P.P., Gupta, M.: Topological design of communication networks using multiobjective genetic optimization. In: Proc. Congress Evolutionary Computation (CEC-2002). (2002) 425-430
9. Kumar, R., Banerjee, N.: Multicriteria network design using evolutionary algorithm. In: Proc. Genetic and Evolutionary Computations Conference (GECCO-03), LNCS 2023. (2003) 2179-2190
10. Laumanns, M., Thiele, L., Zitzler, E., Welzl, E., Deb, K.: Running time analysis of multiobjective evolutionary algorithms on a discrete optimization problem. In: Proc. Parallel Problem Solving from Nature (PPSN VII), LNCS 2439. (2002) 44-53
11. Laumanns, M., Thiele, L., Zitzler, E.: Running time analysis of evolutionary algorithms on pseudo-boolean functions. IEEE Trans. Evolutionary Computation 8 (2004) 170-182
12. Laumanns, M., Thiele, L., Zitzler, E.: Running time analysis of evolutionary algorithms on a simplified multiobjective knapsack problem. Natural Computing 3 (2004) 37-51
13. Droste, S., Jansen, T., Wegener, I.: On the Optimization of Unimodal Functions with the (1+1) Evolutionary Algorithm. In: Proc. Parallel Problem Solving from Nature (PPSN-V), LNCS 1498. (1998) 13-22
14. th Int. Colloquium Automata, Languages and Programming (ICALP 2003), LNCS 2719. (2003) 1068-1079
15. Droste, S., Jansen, T., Wegener, I.: On the Analysis of the (1+1) Evolutionary Algorithm. Theoretical Computer Science 276 (2002) 51-81
16. Gamier, J., Kallel, L., Schoenauer, M.: Rigorous hitting times for binary mutations. Evolutionary Computation 7 (1999) 167-203
17. Rudolph, G.: How mutation and selection solve long path problems in polynomial expected time. Evolutionary Computation 4 (1996) 207-211
18. Wegener, I., Witt, C.: On the analysis of a simple evolutionary algorithm on quadratic pseudo-boolean functions. J. Discrete Algorithms (2002)
19. Jansen, T., Wegener, I.: The analysis of evolutionary algorithms: a proof that crossover really can help. Algorithmica 34 (2002) 47-66
20. Coello, C.A.C., Veldhuizen, D.A.V., Lamont, G.B.: Evolutionary Algorithms for Solving Multiojective Problems. Boston, MA: Kluwer (2002)
21. Deb, K.: Multiobjective Optimization Using Evolutionary Algorithms. Chichester, UK: Wiley (2001)
22. Rudolph, G.: Convergence Properties of Evolutionary Algorithms. Hamburg, Germany: Verlag Dr. Kovač (1997)
23. Rudolph, G.: Evolutionary search for minimal elements in partially ordered finite sets. In: Proc. Annual Conference on Evolutionary Programming. (1998) 345-353
24. Rudolph, G., Agapie, A.: Convergence properties of some multiobjective evolutionary algorithms. In: Proc. Congress on Evolutionary Computation. (2000) 1010-1016
25. Giel, O.: Runtime analysis for a simple multiobjective evolutionary algorithm. Tech-Report, Dept. Computer Science, Univ. Dortmund, Germany (2003)
26. Thierens, D.: Convergence time analysis for the multi-objective counting ones problem. In: Proc. Conf. Evolutionary Multiobjective Optimization (EMO-03), LNCS 2632. (2003) 355-364
27. Asho, I.: Interactive Knapsacks: Theory and Applications. Ph.D. Thesis, Tech Report No.: A-2002-13, Department of Computer and Information Sciences, University of Tampere (2002)
28. Frieze, A., Clarke, M.: Approximation algorithms for m-dimensional 0-1 knapsack problem: Worst case and probabilistic analysis. European J. Operations Research 15 (1984) 100-109
29. Erlebach, T., Kellerer, H., Pferschy, U.: Approximating Multiobjective Knapsack Problems. Management Science 48 (2002) 1603-1612
30. Ibarra, O.H., Kim, C.E.: Fast approximation algorithms for the knapsack and sum of subset problem. J. ACM 22 (1984) 463-468
31. Beyer, H.G., Schwefel, H.P., Wegener, I.: How to Analyse Evolutionary Algorithms? Theoretical Computer Science 287 (2002) 101-130
32. Scharnow, J., Tinnefeid, K., Wegener, I.: Fitness landscapes based on sorting and shortest path problems. In: Proc. Parallel Problem Solving From Nature (PPSN VII), LNCS 2439. (2002) 54-63
33. Droste, S., Jansen, T., Tinnefeld, K., Wegener, I.: A new framework for the valuation of algorithms for black-box optimization. In: Proc. Foundations of Genetic Algorithms Workshop (FOGA VII). (2002) 197-214
34. Deb, K., Others: A Fast Non-Dominated Sorting Genetic Algorithm for Multiobjective Optimization: NSGA-II. In: Proc. Parallel Problem Solving from Nature (PPSN-VI), LNCS. (2000) 849-858
35. Knowles, J.D., Corne, D.W.: Approximating the Non-Dominated Front Using the Pareto Achieved Evolution Strategy. Evolutionary Computation 8 (2000) 149-172
36. Kumar, R., Rockett, P.I.: Improved Sampling of the Pareto-front in Multiobjective Genetic Optimization by Steady-State Evolution: A Pareto Converging Genetic Algorithm. Evolutionary Computation 10 (2002) 283-314
37. Zitzler, E., Laumanns, M., , Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm. In: Proc. Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems (EUROGEN). (2001)
38. Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C.M., da Fonseca, V.G.: Performance assessment of multiobjective optimizers: An analysis and review. IEEE Trans. Evolutionary Computation 7 (2003) 117-132