Approximating the knee of an MOP with stochastic search algorithms

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

Volumn 5199 LNCS, Issue , 2008, Pages 795-804

  Schütze, Oliver ^a   Laumanns, Marco ^b   Coello Coello, Carlos A ^a  

^a DEPARTAMENTO DE FÍSICA   (Mexico)

^b ETH ZURICH   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

EVOLUTIONARY ALGORITHMS; LEARNING ALGORITHMS; MULTIOBJECTIVE OPTIMIZATION; ALGORITHMS; DECISION MAKING; OPTIMIZATION; STOCHASTIC SYSTEMS;

APPLICATIONS.; DECISION MAKERS; OBJECTIVE OPTIMIZATIONS; SEARCH ALGORITHMS; STOCHASTIC SEARCH ALGORITHMS;

PROBLEM SOLVING;

BI-OBJECTIVE OPTIMIZATION; DECISION MAKERS; KNEE REGIONS; MULTI OBJECTIVE; SEARCH ALGORITHMS; STOCHASTIC SEARCH ALGORITHMS;

EID: 56449119325     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-87700-4_79     Document Type: Conference Paper

References (14)

1. Branke, J., Deb, K., Dierolf, H., Osswald, M.: Finding knees in multi-objective optimization. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) PPSN VIII 2004. LNCS, vol. 3242, pp. 722-731. Springer, Heidelberg (2004)
2. Branke, J., Kaussler, T., Schmeck, H.: Guidance in evolutionary multi-objective optimization. Advances in Engineering Software 32, 499-507 (2001)
3. Das, I.: On characterizing the "knee" of the Pareto curve based on Normal Boundary Intersection. Structural Optimization 18, 107-115 (1999)
4. Deb, K.: Multi-objective evolutionary algorithms: introducing bias among pareto-optimal solutions, pp. 263-292 (2003)
5. di Pierro, F., Khu, S.F., Savic, D.A.: An investigation on preference order ranking scheme for multiobjective evolutionary optimization, IEEE Transactions on Evolutionary Computation 11(1), 17-45 (2007)
6. Handl, J., Knowles, J.: Exploiting the trade-off: the benefits of multiple objectives in data clustering. In: Proceedings of the Third International Conference on Evolutionary Multicriterion Optimization, pp. 547-560
7. Ishibuchi, H., Nojima, Y., Narukawa, K., Doi, T.: Incorporation of decision maker's preference into evolutionary multiobjective optimization algorithms. In: GECCO, pp. 741-742 (2006)
8. Kukkonen, S., Deb, K.: A fast and effective method for pruning of non-dominated solutions in many-objective problems. In: PPSN, pp. 553-562 (2006)
9. Mattson, C.A., Mullur, A.A., Messac, A.: Smart Pareto filter: Obtaining a minimal representation of multiobjective design space. Engineering Optimization 36, 721-740 (2004)
10. Mehnen, J., Trautmann, H.: Integration of expert's preferences in pareto optimization by desirability function techniques. In: Proceeding's of the 5th CIRP International Seminar on Intelligent Computation in Manufacturing Engineering (CIRP ICME 2006) (2006)
11. Miettinen, K.: Nonlinear Multiobjective Optimization. Kluwer Academic Publishers, Dordrecht (1999)
12. Rachmawati, L., Srinivasan, D.: A multi-objective evolutionary algorithm with weighted-sum niching for convergence on knee regions. In: Proceedings of the 8th annual conference on Genetic and evolutionary computation, pp. 749-750 (2006)
13. Rachmawati, L., Srinivasan, D.: A Multi-Objective Genetic Algorithm with Controllable Convergence on Knee Regions. In: IEEE Congress on Evolutionary Computation, CEC 2006, pp. 1916-1923 (2006)
14. Witting, K., Hessel-von Molo, M.: Private communication (2006)