메뉴 건너뛰기




Volumn , Issue , 2008, Pages 177-200

Reducing Bloat in GP with Multiple Objectives

Author keywords

[No Author keywords available]

Indexed keywords


EID: 85128837456     PISSN: 16197127     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-540-72964-8_9     Document Type: Chapter
Times cited : (17)

References (32)
  • 5
    • 85128873256 scopus 로고    scopus 로고
    • T. Blickle. Evolving Compact Solutions in Genetic Programming: A Case Study. In H. M. Voigt et al., editors, PPSN IV, pages 564–573. Springer-Verlag, 1996.
    • T. Blickle. Evolving Compact Solutions in Genetic Programming: A Case Study. In H. M. Voigt et al., editors, PPSN IV, pages 564–573. Springer-Verlag, 1996.
  • 7
    • 85128840489 scopus 로고    scopus 로고
    • E. D. De Jong, R. A. Watson, and J. B. Pollack. Reducing Bloat and Promoting Diversity using Multi-Objective Methods. In L. Spector et al., editors, Genetic and Evolutionary Computation Conference (GECCO 2001), pages 11–18. Morgan Kaufmann Publishers, 2001.
    • E. D. De Jong, R. A. Watson, and J. B. Pollack. Reducing Bloat and Promoting Diversity using Multi-Objective Methods. In L. Spector et al., editors, Genetic and Evolutionary Computation Conference (GECCO 2001), pages 11–18. Morgan Kaufmann Publishers, 2001.
  • 8
    • 85128832086 scopus 로고    scopus 로고
    • K. Deb, S. Agrawal, A. Pratap, and T. Meyarivan. A fast elitist nondominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In M. Schoenauer et al., editors, Parallel Problem Solving from Nature (PPSN VI), Lecture Notes in Computer Science Vol. 1917, pages 849–858. Springer, 2000.
    • K. Deb, S. Agrawal, A. Pratap, and T. Meyarivan. A fast elitist nondominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In M. Schoenauer et al., editors, Parallel Problem Solving from Nature (PPSN VI), Lecture Notes in Computer Science Vol. 1917, pages 849–858. Springer, 2000.
  • 9
    • 3543074493 scopus 로고    scopus 로고
    • Selection Based on the Pareto Nondomination Criterion for Controlling Code Growth in Genetic Programming
    • A. Ekárt and S. Z. Németh. Selection Based on the Pareto Nondomination Criterion for Controlling Code Growth in Genetic Programming. Genetic Programming and Evolvable Machines, 2:61–73, 2001.
    • (2001) Genetic Programming and Evolvable Machines , vol.2 , pp. 61-73
    • Ekárt, A.1    Németh, S.Z.2
  • 10
    • 23044534709 scopus 로고    scopus 로고
    • Expression Inference-Genetic Symbolic Classification Integrated with Non-linear Coefficient Optimisation
    • A. Hunter. Expression Inference-Genetic Symbolic Classification Integrated with Non-linear Coefficient Optimisation. In AISC 02, LNCS. Springer, 2002.
    • (2002) AISC 02, LNCS. Springer
    • Hunter, A.1
  • 12
    • 85128852782 scopus 로고    scopus 로고
    • T. Kalganova and J. F. Miller. Evolving More Efficient Digital Circuits by Allowing Circuit Layout Evolution and Multi-Objective Fitness. In A. Sto-ica et al., editors, Proceedings of the 1st NASA/DoD Workshop on Evolvable Hardware (EH’99), pages 54–63, Piscataway, NJ, 1999, 1999. IEEE Computer Society Press.
    • T. Kalganova and J. F. Miller. Evolving More Efficient Digital Circuits by Allowing Circuit Layout Evolution and Multi-Objective Fitness. In A. Sto-ica et al., editors, Proceedings of the 1st NASA/DoD Workshop on Evolvable Hardware (EH’99), pages 54–63, Piscataway, NJ, 1999, 1999. IEEE Computer Society Press.
  • 15
    • 85128849635 scopus 로고    scopus 로고
    • M. Kotanchek, G. Smits, and E. Vladislavleva. Pursuing the Pareto Paradigm Tournaments, Algorithm Variations & Ordinal Optimization. In R. L. Riolo, T. Soule, and B. Worzel, editors, Genetic Programming Theory and Practice IV, volume 5 of Genetic and Evolutionary Computation, chapter 3. Springer, 2006.
    • M. Kotanchek, G. Smits, and E. Vladislavleva. Pursuing the Pareto Paradigm Tournaments, Algorithm Variations & Ordinal Optimization. In R. L. Riolo, T. Soule, and B. Worzel, editors, Genetic Programming Theory and Practice IV, volume 5 of Genetic and Evolutionary Computation, chapter 3. Springer, 2006.
  • 18
    • 85128852879 scopus 로고    scopus 로고
    • W. B. Langdon. Quadratic Bloat in Genetic Programming. In D. Whitley et al., editors, GECCO 2000, pages 451–458, Las Vegas, Nevada, USA, 10-12 2000. Morgan Kaufmann. ISBN 1-55860-708-0.
    • W. B. Langdon. Quadratic Bloat in Genetic Programming. In D. Whitley et al., editors, GECCO 2000, pages 451–458, Las Vegas, Nevada, USA, 10-12 2000. Morgan Kaufmann. ISBN 1-55860-708-0.
  • 22
    • 27144544741 scopus 로고    scopus 로고
    • Multi-objective techniques in genetic programming for evolving classifiers
    • , pages , IEEE
    • D. Parrot, L. Xiandong, and V. Ciesielski. Multi-objective techniques in genetic programming for evolving classifiers. In CEC 05, pages 1141–1148. IEEE, 2005.
    • (2005) CEC 05 , pp. 1141-1148
    • Parrot, D.1    Xiandong, L.2    Ciesielski, V.3
  • 23
    • 85128823830 scopus 로고    scopus 로고
    • K. Rodríguez-Vázquez, C. M. Fonseca, and P. J. Fleming. Multiobjective genetic programming: A nonlinear system identification application. In J. R. Koza, editor, Late Breaking Papers at the 1997 Genetic Programming Conference, pages 207–212, Stanford University, CA, USA, 13–16 1997. Stanford Bookstore. ISBN 0-18-206995-8.
    • K. Rodríguez-Vázquez, C. M. Fonseca, and P. J. Fleming. Multiobjective genetic programming: A nonlinear system identification application. In J. R. Koza, editor, Late Breaking Papers at the 1997 Genetic Programming Conference, pages 207–212, Stanford University, CA, USA, 13–16 1997. Stanford Bookstore. ISBN 0-18-206995-8.
  • 26
    • 0032241894 scopus 로고    scopus 로고
    • Effects of Code Growth and Parsimony Pressure on Populations in Genetic Programming
    • T. Soule and J. A. Foster. Effects of Code Growth and Parsimony Pressure on Populations in Genetic Programming. Evoluationary Computation, 6(4): 293–309, 1999.
    • (1999) Evoluationary Computation , vol.6 , Issue.4 , pp. 293-309
    • Soule, T.1    Foster, J.A.2
  • 27
    • 3543051689 scopus 로고    scopus 로고
    • Automated Discovery of Numerical Approximation Formulae via Genetic Programming
    • M. Streeter and L. A. Becker. Automated Discovery of Numerical Approximation Formulae via Genetic Programming. Genetic Programming and Evolvable Machines, 4(3):255–286, 2003.
    • (2003) Genetic Programming and Evolvable Machines , vol.4 , Issue.3 , pp. 255-286
    • Streeter, M.1    Becker, L.A.2
  • 28
    • 0002796467 scopus 로고
    • Balancing Accuracy and Parsimony in Genetic Programming
    • B.-T. Zhang and H. Mühlenbein. Balancing Accuracy and Parsimony in Genetic Programming. Evoluationary Computation, 3(1):17–38, 1995.
    • (1995) Evoluationary Computation , vol.3 , Issue.1 , pp. 17-38
    • Zhang, B.-T.1    Mühlenbein, H.2
  • 29
    • 32444433452 scopus 로고    scopus 로고
    • Evolving optimal feature extraction using multiobjective genetic programming: A methodology and preliminary study on edge detection
    • , pages , New York, NY, USA, ACM Press
    • Y. Zhang and P. I. Rockett. Evolving optimal feature extraction using multiobjective genetic programming: a methodology and preliminary study on edge detection. In GECCO 05, pages 795–802, New York, NY, USA, 2005. ACM Press.
    • (2005) GECCO 05 , pp. 795-802
    • Zhang, Y.1    Rockett, P.I.2
  • 30
    • 85128844258 scopus 로고    scopus 로고
    • E. Zitzler. Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications. PhD thesis, Swiss Federal Institute of Technology (ETH) Zürich, Switzerland, 1999.
    • E. Zitzler. Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications. PhD thesis, Swiss Federal Institute of Technology (ETH) Zürich, Switzerland, 1999.
  • 31
    • 85128817067 scopus 로고    scopus 로고
    • E. Zitzler, M. Laumanns, and L. Thiele. SPEA2: Improving the Strength Pareto Evolutionary Algorithm for Multiobjective Optimization. In K. Giannakoglou et al., editors, Evolutionary Methods for Design, Optimisation and Control with Application to Industrial Problems (EUROGEN 2001), pages 95–100. International Center for Numerical Methods in Engineering (CIMNE), 2002.
    • E. Zitzler, M. Laumanns, and L. Thiele. SPEA2: Improving the Strength Pareto Evolutionary Algorithm for Multiobjective Optimization. In K. Giannakoglou et al., editors, Evolutionary Methods for Design, Optimisation and Control with Application to Industrial Problems (EUROGEN 2001), pages 95–100. International Center for Numerical Methods in Engineering (CIMNE), 2002.
  • 32
    • 0033318858 scopus 로고    scopus 로고
    • Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach
    • E. Zitzler and L. Thiele. Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach. IEEE Transactions on Evolutionary Computation, 3(4):257–271, 1999.
    • (1999) IEEE Transactions on Evolutionary Computation , vol.3 , Issue.4 , pp. 257-271
    • Zitzler, E.1    Thiele, L.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.