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Volumn , Issue , 2013, Pages 623-630

Revisiting the NSGA-II crowding-distance computation

Author keywords

Crowding distance; Multi objective evolutionary algorithms; NSGA II

Indexed keywords

BENCH-MARK PROBLEMS; CROWDING DISTANCE; MULTI OBJECTIVE EVOLUTIONARY ALGORITHMS; NSGA-II; PARETO FRONT;

EID: 84883079141     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2463372.2463456     Document Type: Conference Paper
Times cited : (69)

References (16)
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    • K. Deb and H. Jain. Handling many-objective problems using an improved NSGA-II procedure. In Evolutionary Computation (CEC), 2012 IEEE Congress on, pages 1-8, June 2012.
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    • Deb, K.1    Jain, H.2
  • 10
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    • Approximating the nondominated front using the pareto archived evolution strategy
    • J. Knowles and D. Corne. Approximating the nondominated front using the pareto archived evolution strategy. Evolutionary computation, 8(2):149-172, 2000.
    • (2000) Evolutionary Computation , vol.8 , Issue.2 , pp. 149-172
    • Knowles, J.1    Corne, D.2
  • 11
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    • Muiltiobjective optimization using nondominated sorting in genetic algorithms
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    • Comparison of multiobjective evolutionary algorithms: Empirical results
    • E. Zitzler, K. Deb, and L. Thiele. Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. Evolutionary Computation, 8(2):173-195, 2000.
    • (2000) Evolutionary Computation , vol.8 , Issue.2 , pp. 173-195
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  • 16
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    • E. Zitzler, M. Laumanns, and L. Thiele. SPEA2: Improving the strength Pareto evolutionary algorithm. Technical Report 103, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH) Zurich, Gloriastrasse 35, CH-8092 Zurich, Switzerland, May 2001.
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.