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Volumn 38, Issue 1, 2000, Pages 9-40

Learnable evolution model: evolutionary processes guided by machine learning

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL METHODS; COMPUTER PROGRAMMING; DATA MINING; ECONOMICS; FUNCTIONS; GENETIC ALGORITHMS; MATHEMATICAL MODELS; OPTIMIZATION; SOFTWARE ENGINEERING;

EID: 0033884705     PISSN: 08856125     EISSN: None     Source Type: Journal    
DOI: 10.1023/a:1007677805582     Document Type: Article
Times cited : (197)

References (57)
  • 1
    • 0000500817 scopus 로고
    • Interactions between learning and evolution
    • C. G. Langton, C. Taylor, J. D. Farmer, & S. Rasmussen (Eds), Addison-Wesley
    • Ackley, D. & Littman, M. (1992). Interactions between learning and evolution, In C. G. Langton, C. Taylor, J. D. Farmer, & S. Rasmussen (Eds), Artificial life II. Addison-Wesley.
    • (1992) Artificial Life II
    • Ackley, D.1    Littman, M.2
  • 5
    • 0001410750 scopus 로고
    • A new factor in evolution
    • Baldwin, J. M. (1896). A new factor in evolution. American naturalist (Vol. 30) (pp. 441-451, 536-553).
    • (1896) American Naturalist , vol.30 , pp. 441-451
    • Baldwin, J.M.1
  • 6
    • 0342672088 scopus 로고    scopus 로고
    • Data-driven constructive induction: A methodology and its applications
    • special issue on feature transformation and subset selection. Huan Liu & Hiroshi Motoda (Eds.), March-April
    • Bloedorn, E. & Michalski, R. S. (1998). Data-driven constructive induction: A methodology and its applications, IEEE Intelligent Systems, special issue on feature transformation and subset selection. Huan Liu & Hiroshi Motoda (Eds.), March-April.
    • (1998) IEEE Intelligent Systems
    • Bloedorn, E.1    Michalski, R.S.2
  • 7
    • 61349151558 scopus 로고    scopus 로고
    • Design and experiments with LEM2 implementation of the Learnable Evolution Model
    • (to appear). George Mason University
    • Cervone, G. & Michalski, R. S. (to appear). Design and experiments with LEM2 implementation of the Learnable Evolution Model, Reports of The Machine Learning and Inference Laboratory, George Mason University.
    • Reports of the Machine Learning and Inference Laboratory
    • Cervone, G.1    Michalski, R.S.2
  • 12
    • 0742294003 scopus 로고    scopus 로고
    • CAM-BRAIN: The evolutionary engineering of a billion neuron artificial brain by 2001 which grows/evolve at electronic speeds inside a cellular automata machine (CAM)
    • Towards evolvable hardware Springer-Verlag
    • de Garis, Hugo. (1996). CAM-BRAIN: The evolutionary engineering of a billion neuron artificial brain by 2001 which grows/evolve at electronic speeds inside a cellular automata machine (CAM). Lecture notes in computer science, Vol. 1062. Towards evolvable hardware (pp 76-98). Springer-Verlag.
    • (1996) Lecture Notes in Computer Science , vol.1062 , pp. 76-98
    • De Garis, H.1
  • 13
    • 0343106380 scopus 로고    scopus 로고
    • Building an Artificial Brain Using an FPGA Based CAM-Brain Machine
    • (to appear). special issue on Artificial life and robotics, artificial brain, brain computing and brainware, North Holland
    • de Garis, H., Korkin, M., Gers, F., Nawa, E., & Hough, M. (to appear). Building an Artificial Brain Using an FPGA Based CAM-Brain Machine, Applied Mathematics and Computation Journal (special issue on Artificial life and robotics, artificial brain, brain computing and brainware, North Holland.
    • Applied Mathematics and Computation Journal
    • De Garis, H.1    Korkin, M.2    Gers, F.3    Nawa, E.4    Hough, M.5
  • 15
    • 0027696338 scopus 로고
    • Using genetic algorithms for concept learning
    • De Jong, K. A., Spears, W. M., & Gordon, F. D. (1993). Using genetic algorithms for concept learning, Machine Learning, 13, 161-188.
    • (1993) Machine Learning , vol.13 , pp. 161-188
    • De Jong, K.A.1    Spears, W.M.2    Gordon, F.D.3
  • 17
    • 0031361611 scopus 로고    scopus 로고
    • Machine-learning research: Four current directions
    • Dietterich, T. G. (1997). Machine-learning research: four current directions, AI Magazine, 18(4).
    • (1997) AI Magazine , vol.18 , Issue.4
    • Dietterich, T.G.1
  • 19
    • 0343106326 scopus 로고
    • AQPLUS: An adaptive random search method for selecting a best set of attributes from a large collection of candidates
    • Department of Computer Science, University of Illinois, Urbana
    • Forsburg, S. (1976). AQPLUS: An adaptive random search method for selecting a best set of attributes from a large collection of candidates, Internal Technical Report, Department of Computer Science, University of Illinois, Urbana.
    • (1976) Internal Technical Report
    • Forsburg, S.1
  • 20
    • 0000662737 scopus 로고
    • Search-intensive concept induction
    • Giordana A. & Neri, F. (1995). Search-intensive concept induction. Evolutionary Computation, 3(4), 375-416.
    • (1995) Evolutionary Computation , vol.3 , Issue.4 , pp. 375-416
    • Giordana, A.1    Neri, F.2
  • 22
    • 0000440954 scopus 로고
    • Lamarckian learning in multi-agent environment
    • R. Belew & L. Booker (Eds.). San Mateo, GA: Morgan Kaufmann
    • Grefenstette, J. (1991). Lamarckian learning in multi-agent environment. In R. Belew & L. Booker (Eds.). Proceedings of the Fourth International Conference on Genetic Algorithms, San Mateo, GA: Morgan Kaufmann (pp. 303-310).
    • (1991) Proceedings of the Fourth International Conference on Genetic Algorithms , pp. 303-310
    • Grefenstette, J.1
  • 23
    • 0027696043 scopus 로고
    • Competition-based induction of decision models from examples
    • Greene D. P. & Smith, S. F. (1993). Competition-based induction of decision models from examples. Machine Learning, 13, 229-257.
    • (1993) Machine Learning , vol.13 , pp. 229-257
    • Greene, D.P.1    Smith, S.F.2
  • 26
    • 0000211184 scopus 로고
    • How learning can guide evolution
    • Hinton, G. E. & Nowlan, S. J. (1987). How learning can guide evolution. Complex Systems, 1, 495-502.
    • (1987) Complex Systems , vol.1 , pp. 495-502
    • Hinton, G.E.1    Nowlan, S.J.2
  • 28
    • 0027696178 scopus 로고
    • A knowledge-intensive genetic algorithm for supervised learning
    • Janikow, C. Z. (1993). A knowledge-intensive genetic algorithm for supervised learning. Machine Learning, 13, 189-228.
    • (1993) Machine Learning , vol.13 , pp. 189-228
    • Janikow, C.Z.1
  • 29
    • 55749094149 scopus 로고    scopus 로고
    • Learning from inconsistent and noisy data: The AQ18 approach
    • 11th International Symposium, ISMIS'99, Warsaw, Poland, Spring
    • Kaufman, K. & Michalski, R. S. (1999). Learning from inconsistent and noisy data: the AQ18 approach, Foundations of Intelligent Systems, 11th International Symposium, ISMIS'99, Warsaw, Poland, Spring.
    • (1999) Foundations of Intelligent Systems
    • Kaufman, K.1    Michalski, R.S.2
  • 34
    • 0003312474 scopus 로고
    • On the quasi-minimal solution of the general covering problem
    • Proceedings of the V International Symposium on Information Processing (FCIP 69), Yugoslavia, Bled
    • Michalski, R. S. (1969). On the quasi-minimal solution of the general covering problem, Proceedings of the V International Symposium on Information Processing (FCIP 69), Yugoslavia, Bled (Vol. A3) Switching Circuits, (pp. 125-128).
    • (1969) Switching Circuits , vol.A3 , pp. 125-128
    • Michalski, R.S.1
  • 35
    • 26644463335 scopus 로고
    • A planar geometrical model for representing multi-dimensional discrete spaces and multiple-valued logic functions
    • University of Illinois at Champaign-Urbana.
    • Michalski, R. S. (1978). A planar geometrical model for representing multi-dimensional discrete spaces and multiple-valued logic functions. Reports of the Department of Computer Science, No. 897, University of Illinois at Champaign-Urbana.
    • (1978) Reports of the Department of Computer Science, No. 897 , vol.897
    • Michalski, R.S.1
  • 37
    • 0000942050 scopus 로고
    • A theory and methodology of inductive learning
    • Michalski, R. S. (1983). A theory and methodology of inductive learning, Artificial Intelligence, 20(2) 111-161.
    • (1983) Artificial Intelligence , vol.20 , Issue.2 , pp. 111-161
    • Michalski, R.S.1
  • 38
    • 0003262526 scopus 로고
    • Inferential theory of learning: Developing foundations for multistrategy learning
    • R. S. Michalski & G. Tecuci (Eds.), San Mateo, CA, Morgan Kaufmann
    • Michalski, R. S. (1994). Inferential theory of learning: developing foundations for multistrategy learning, InR. S. Michalski & G. Tecuci (Eds.), Machine learning: a multistrategy approach (Vol. IV) San Mateo, CA, Morgan Kaufmann.
    • (1994) Machine Learning: A Multistrategy Approach , vol.4
    • Michalski, R.S.1
  • 40
    • 0005107221 scopus 로고    scopus 로고
    • Natural induction: Theory, methodology and its application to machine learning and data mining
    • (to appear). George Mason University
    • Michalski, R. S. (to appear). Natural induction: theory, methodology and its application to machine learning and data mining. Reports of the Machine Learning and Inference Laboratory, George Mason University.
    • Reports of the Machine Learning and Inference Laboratory
    • Michalski, R.S.1
  • 44
    • 0003915394 scopus 로고
    • The AQ15 inductive learning system: An overview and experiments
    • No. 86-20, UIUCDCS-R-86-1260, Department of Computer Science, University of Illinois, Urbana
    • Michalski, R. S., Mozetic, I., Hong, J., & Lavrac, N. (1986). The AQ15 inductive learning system: an overview and experiments. Reports of the Intelligent Systems Group, No. 86-20, UIUCDCS-R-86-1260, Department of Computer Science, University of Illinois, Urbana.
    • (1986) Reports of the Intelligent Systems Group
    • Michalski, R.S.1    Mozetic, I.2    Hong, J.3    Lavrac, N.4
  • 45
    • 0343977975 scopus 로고    scopus 로고
    • Initial experiments with the LEM1 learnable evolution model: An application to function optimization and evolvable hardware
    • George Mason University
    • Michalski, R. S. & Zhang, Q. (1999). Initial experiments with the LEM1 learnable evolution model: an application to function optimization and evolvable hardware. Reports of the Machine Learning and Inference Laboratory, George Mason University.
    • (1999) Reports of the Machine Learning and Inference Laboratory
    • Michalski, R.S.1    Zhang, Q.2
  • 47
    • 0031235612 scopus 로고    scopus 로고
    • Does machine learning really work
    • Mitchell, T. M. (1997). Does machine learning really work. AI Magazine, 78(3).
    • (1997) AI Magazine , vol.78 , Issue.3
    • Mitchell, T.M.1
  • 49
    • 84958962163 scopus 로고
    • Controlling crossover through inductive learning
    • Y. Davidor, H. P. Schwefel & R. Manner (Eds.), LNVS Springer-Verlag
    • Sebag, M. & Schoenauer, M. (1994). Controlling crossover through inductive learning In Y. Davidor, H. P. Schwefel & R. Manner (Eds.), Proceedings of the 3rd Conference on Parallel Problem Solving from Nature, LNVS (Vol. 866) (pp. 209-218). Springer-Verlag.
    • (1994) Proceedings of the 3rd Conference on Parallel Problem Solving from Nature , vol.866 , pp. 209-218
    • Sebag, M.1    Schoenauer, M.2
  • 52
    • 0000865580 scopus 로고
    • Cost-sensitive classification: Empirical evaluation of a hybrid genetic decision tree induction algorithm
    • Turney, P. D. (1995). Cost-sensitive classification: empirical evaluation of a hybrid genetic decision tree induction algorithm. Journal of Artificial Intelligence Research, 2, 369-409.
    • (1995) Journal of Artificial Intelligence Research , vol.2 , pp. 369-409
    • Turney, P.D.1
  • 56
    • 0028406563 scopus 로고
    • Nonlinear parameter estimation via the genetic algorithm
    • Yao, L. & Sethares, W. (1994). Nonlinear parameter estimation via the genetic algorithm. IEEE Transactions on Signal Processing, 42(4), 927-935.
    • (1994) IEEE Transactions on Signal Processing , vol.42 , Issue.4 , pp. 927-935
    • Yao, L.1    Sethares, W.2
  • 57
    • 0343106173 scopus 로고    scopus 로고
    • Knowledge visualizer: A software system for visualizing data, patterns and their relationships
    • MLI 97-14, George Mason University, Fairfax, VA
    • Zhang, Q. (1997). Knowledge visualizer: a software system for visualizing data, patterns and their relationships. Reports of the Machine Learning and Inference Laboratory, MLI 97-14, George Mason University, Fairfax, VA.
    • (1997) Reports of the Machine Learning and Inference Laboratory
    • Zhang, Q.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.