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Volumn 8, Issue 2, 2009, Pages 151-173

Multi-objective bayesian artificial immune system: Empirical evaluation and comparative analyses

Author keywords

Artificial immune system; Bayesian network; Building blocks; Combinatorial optimization; Multi objective optimization

Indexed keywords


EID: 67349166512     PISSN: 15701166     EISSN: 15729214     Source Type: Journal    
DOI: 10.1007/s10852-009-9108-2     Document Type: Conference Paper
Times cited : (13)

References (44)
  • 1
    • 0023392436 scopus 로고
    • The clonal selection theory
    • G.L. Ada G.J.V. Nossal 1987 The clonal selection theory Sci. Am. 257 2 50 57
    • (1987) Sci. Am. , vol.257 , Issue.2 , pp. 50-57
    • Ada, G.L.1    Nossal, G.J.V.2
  • 3
    • 0001955592 scopus 로고    scopus 로고
    • Using optimal dependency-trees for combinational optimization
    • San Francisco
    • Baluja, S., Davies, S.: Using optimal dependency-trees for combinational optimization. In: Proc. of the 14th Int. Conf. on Machine Learning, pp. 30-38. San Francisco (1997)
    • (1997) Proc. of the 14th Int. Conf. on Machine Learning , pp. 30-38
    • Baluja, S.1    Davies, S.2
  • 6
    • 61449207825 scopus 로고    scopus 로고
    • BAIS: A Bayesian artificial immune system for the effective handling of building blocks
    • in press
    • Castro, P.A.D., Von Zuben, F.J.: BAIS: A Bayesian artificial immune system for the effective handling of building blocks. Inf. Sci. (2009, in press)
    • (2009) Inf. Sci.
    • Castro, P.A.D.1    Von Zuben, F.J.2
  • 12
    • 3543056828 scopus 로고    scopus 로고
    • An approach to solve multiobjective optimization problems based on an artificial immune system
    • Coello Coello, C., Cortés, N.C.: An approach to solve multiobjective optimization problems based on an artificial immune system. In: Proc. of the 1st Int. Conf. on Artificial Immune System, pp. 212-221 (2002)
    • (2002) Proc. of the 1st Int. Conf. on Artificial Immune System , pp. 212-221
    • Coello Coello, C.1
  • 13
    • 17444430405 scopus 로고    scopus 로고
    • Solving multiobjective optimization problems using an artificial immune system
    • C. Coello Coello N.C. Cortés 2005 Solving multiobjective optimization problems using an artificial immune system Genet. Program. Evolv. Mach. 6 2 163 190
    • (2005) Genet. Program. Evolv. Mach. , vol.6 , Issue.2 , pp. 163-190
    • Coello Coello, C.1    Cortés, N.C.2
  • 14
    • 34249832377 scopus 로고
    • A Bayesian method for the induction of probabilistic networks from data
    • G. Cooper E. Herskovits 1992 A Bayesian method for the induction of probabilistic networks from data Mach. Learn. 9 309 347
    • (1992) Mach. Learn. , vol.9 , pp. 309-347
    • Cooper, G.1    Herskovits, E.2
  • 15
    • 33846309260 scopus 로고    scopus 로고
    • Advances in artificial immune systems
    • DOI 10.1109/CI-M.2006.248056
    • D. Dasgupta 2006 Advances in artificial immune systems IEEE Comput. Intell. Mag. 1 40 49 (Pubitemid 46118249)
    • (2006) IEEE Computational Intelligence Magazine , vol.1 , Issue.4 , pp. 40-43
    • Dasgupta, D.1
  • 17
    • 0036613006 scopus 로고    scopus 로고
    • Learning and optimization using the clonal selection principle
    • L.N. de Castro F.J. Von Zuben 2002 Learning and optimization using the clonal selection principle IEEE Trans. Evol. Comput. 6 3 239 251
    • (2002) IEEE Trans. Evol. Comput. , vol.6 , Issue.3 , pp. 239-251
    • De Castro, L.N.1    Von Zuben, F.J.2
  • 19
    • 0033185714 scopus 로고    scopus 로고
    • Multi-objective genetic algorithms: Problem difficulties and construction of test problems
    • K. Deb 1999 Multi-objective genetic algorithms: problem difficulties and construction of test problems Evol. Comput. 7 205 230
    • (1999) Evol. Comput. , vol.7 , pp. 205-230
    • Deb, K.1
  • 21
    • 24344437186 scopus 로고    scopus 로고
    • Omni-optimizer: A procedure for single and multi-objective optimization
    • Deb, K., Tiwari, S.: Omni-optimizer: a procedure for single and multi-objective optimization. In: Proc. of the of EMO, pp. 47-61 (2005)
    • (2005) Proc. of the of EMO , pp. 47-61
    • Deb, K.1    Tiwari, S.2
  • 22
    • 33750313171 scopus 로고    scopus 로고
    • VIS: An artificial immune network for multi-objective optimization
    • DOI 10.1080/03052150600880706, PII U33283783QQ71P54
    • F. Freschi M. Repetto 2006 VIS: an artificial immune network for multi-objective optimization Engin. Optim. 38 975 996 (Pubitemid 44627568)
    • (2006) Engineering Optimization , vol.38 , Issue.8 , pp. 975-996
    • Freschi, F.1    Repetto, M.2
  • 23
    • 0001907722 scopus 로고
    • Rapid accurate optimization of difficult problems using fast messy genetic algorithms
    • Morgan Kaufmann San Francisco
    • Goldberg, D.E., Deb, K., Kargupta, H., Harik, G.: Rapid accurate optimization of difficult problems using fast messy genetic algorithms. In: Proc. of the Fifth Int. Conf. on Genetic Algorithms, pp. 56-64. Morgan Kaufmann, San Francisco (1993)
    • (1993) Proc. of the Fifth Int. Conf. on Genetic Algorithms , pp. 56-64
    • Goldberg, D.E.1    Deb, K.2    Kargupta, H.3    Harik, G.4
  • 24
    • 0000904077 scopus 로고
    • Messy genetic algorithms: Motivation, analysis, and first results
    • D.E. Goldberg G. Korb K. Deb 1989 Messy genetic algorithms: motivation, analysis, and first results Complex Syst. 3 493 530
    • (1989) Complex Syst. , vol.3 , pp. 493-530
    • Goldberg, D.E.1    Korb, G.2    Deb, K.3
  • 26
    • 0001247275 scopus 로고    scopus 로고
    • Propagating uncertainty in Bayesian networks by probabilistic logic sampling
    • M. Henrion 1998 Propagating uncertainty in Bayesian networks by probabilistic logic sampling Uncertainty Artif. Intell. 2 149 163
    • (1998) Uncertainty Artif. Intell. , vol.2 , pp. 149-163
    • Henrion, M.1
  • 28
    • 0015956495 scopus 로고
    • Towards a network theory of the immune system
    • N.K. Jerne 1974 Towards a network theory of the immune system Ann. Immunol. (Inst. Pasteur) 125C 373 389
    • (1974) Ann. Immunol. (Inst. Pasteur) , vol.125 C , pp. 373-389
    • Jerne, N.K.1
  • 30
    • 0038296200 scopus 로고    scopus 로고
    • MOIA: Multi-objective immune algorithm
    • G.-C. Luh C.-H. Chueh W.-M. Liu 2003 MOIA: multi-objective immune algorithm Eng. Optim. 35 2 143 164
    • (2003) Eng. Optim. , vol.35 , Issue.2 , pp. 143-164
    • Luh, G.-C.1    Chueh, C.-H.2    Liu, W.-M.3
  • 32
    • 0033258156 scopus 로고    scopus 로고
    • FDA-a scalable evolutionary algorithm for the optimization of additively decomposed functions
    • H. Mühlenbein T. Mahnig 1999 FDA-a scalable evolutionary algorithm for the optimization of additively decomposed functions Evol. Comput. 7 353 376
    • (1999) Evol. Comput. , vol.7 , pp. 353-376
    • Mühlenbein, H.1    Mahnig, T.2
  • 33
    • 0003115715 scopus 로고
    • Multicriteria optimization for engineering design
    • Academic London
    • Osyczka, A.: Multicriteria optimization for engineering design. In: Gero, J.S. (ed.) Design Optimization, pp. 193-227. Academic, London (1985)
    • (1985) Design Optimization , pp. 193-227
    • Osyczka, A.1    Gero, J.S.2
  • 36
    • 0001171707 scopus 로고    scopus 로고
    • BOA: The Bayesian optimization algorithm
    • Pelikan, M., Goldberg, D.E., Cantú-Paz, E.: BOA: the Bayesian optimization algorithm. In: Proc. of the Genetic and Evol. Comput. Conference, vol. I., pp. 525-532 (1999)
    • (1999) Proc. of the Genetic and Evol. Comput. Conference , vol.1 , pp. 525-532
    • Pelikan, M.1
  • 37
    • 0036180213 scopus 로고    scopus 로고
    • A survey of optimization by building and using probabilistic models
    • M. Pelikan D. Goldberg F. Lobo 2002 A survey of optimization by building and using probabilistic models Comput. Optim. Appl. 21 1 5 20
    • (2002) Comput. Optim. Appl. , vol.21 , Issue.1 , pp. 5-20
    • Pelikan, M.1    Goldberg, D.2    Lobo, F.3
  • 39
    • 15544385794 scopus 로고    scopus 로고
    • Globally multimodal problem optimization via an estimation of distribution algorithm based on unsupervised learning of Bayesian networks
    • J.M. Peña J.A. Lozano P. Larrañaga 2005 Globally multimodal problem optimization via an estimation of distribution algorithm based on unsupervised learning of Bayesian networks Evol. Comput. 13 43 66
    • (2005) Evol. Comput. , vol.13 , pp. 43-66
    • Peña, J.M.1    Lozano, J.A.2    Larrañaga, P.3
  • 42
    • 0033208122 scopus 로고    scopus 로고
    • Immune network simulations in multicriterion design
    • J. Yoo P. Hajela 1999 Immune network simulations in multicriterion design Struct. Optim. 18 85 94
    • (1999) Struct. Optim. , vol.18 , pp. 85-94
    • Yoo, J.1    Hajela, P.2
  • 43
    • 0034199979 scopus 로고    scopus 로고
    • Comparison of multiobjective evolutionary algorithms: Empirical results
    • E. Zitzler K. Deb L. Thiele 2000 Comparison of multiobjective evolutionary algorithms: empirical results Evol. Comput. 8 2 173 195
    • (2000) Evol. Comput. , vol.8 , Issue.2 , pp. 173-195
    • Zitzler, E.1    Deb, K.2    Thiele, L.3
  • 44
    • 0033318858 scopus 로고    scopus 로고
    • Multiobjective evolutionary algorithms: A comparative case study and the strength pareto approach
    • E. Zitzler L. Thiele 1999 Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach IEEE Trans. Evol. Comput. 3 4 257 271
    • (1999) IEEE Trans. Evol. Comput. , vol.3 , Issue.4 , pp. 257-271
    • Zitzler, E.1    Thiele, L.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.