-
1
-
-
0003984832
-
-
Technical Report No. CMU-CS-94-163, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
-
Baluja, S.: Population based incremental learning: A method for integrating genetic search based function optimization and competitive learning. Technical Report No. CMU-CS-94-163, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA (1994).
-
(1994)
Population Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
-
-
Baluja, S.1
-
2
-
-
85011779332
-
Removing the genetics from standard genetic algorithm
-
A. Prieditis and S. Russell, editors, Morgan Kaufmann
-
Baluja, S. and Caruana, R.: Removing the genetics from standard genetic algorithm. In A. Prieditis and S. Russell, editors, Proceedings of the International Conference on Machine Learning, Morgan Kaufmann, (1995) 38-46.
-
(1995)
Proceedings of the International Conference on Machine Learning
, pp. 38-46
-
-
Baluja, S.1
Caruana, R.2
-
3
-
-
0003858748
-
-
Technical Report No. CMU-CS-97-107, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
-
Baluja, S. and Davies, S.: Using optimal dependency trees for combinatorial optimization: Learning the structure of search space. Technical Report No. CMU-CS-97-107, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA (1997).
-
(1997)
Using Optimal Dependency Trees for Combinatorial Optimization: Learning the Structure of Search Space
-
-
Baluja, S.1
Davies, S.2
-
4
-
-
15544375067
-
Statistical Machine Learning and Combinatorial Optimization
-
Kallel, L., Naudts, B. and Rogers, A., editors, Springer
-
Berny, A.: Statistical Machine Learning and Combinatorial Optimization. In Kallel, L., Naudts, B. and Rogers, A., editors, Theoretical Aspects of Evolutionary Computing, Springer (2001).
-
(2001)
Theoretical Aspects of Evolutionary Computing
-
-
Berny, A.1
-
7
-
-
0000904077
-
Messy genetic algorithms: Motivation, analysis and first results
-
Goldberg, D.E., Korb, B. and Deb, K.: Messy genetic algorithms: Motivation, analysis and first results. Complex Systems 3(5) (1989) 493-530.
-
(1989)
Complex Systems
, vol.3
, Issue.5
, pp. 493-530
-
-
Goldberg, D.E.1
Korb, B.2
Deb, K.3
-
8
-
-
0011847403
-
Mathematical modeling of discrete estimation of distribution algorithms
-
P. Larrañaga and J.A. Lozano, editors, Kluwer Academic Publishers, Boston
-
González, C., Lozano, J.A. and Larrañaga, P.: Mathematical modeling of discrete estimation of distribution algorithms. In P. Larrañaga and J.A. Lozano, editors, Estimation of Distribution Algorithms: A New Tool for Evolutionary Optimization. Kluwer Academic Publishers, Boston(2001).
-
(2001)
Estimation of Distribution Algorithms: A New Tool for Evolutionary Optimization
-
-
González, C.1
Lozano, J.A.2
Larrañaga, P.3
-
9
-
-
0003826269
-
-
IlliGAL Report No. 97005, Illinois Genetic Algorithms Laboratory, University of Illinois, Urbana, Illinois, USA
-
Harik, G.: Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms. IlliGAL Report No. 97005, Illinois Genetic Algorithms Laboratory, University of Illinois, Urbana, Illinois, USA (1997).
-
(1997)
Learning Gene Linkage to Efficiently Solve Problems of Bounded Difficulty Using Genetic Algorithms
-
-
Harik, G.1
-
10
-
-
0003738581
-
-
Illigal Report No. 99010, Illinois Genetic Algorithm Laboratory, University of Illinois, Urbana, Illinois, USA
-
Harik, G.: Linkage learning via probabilistic modeling in the ECGA. Illigal Report No. 99010, Illinois Genetic Algorithm Laboratory, University of Illinois, Urbana, Illinois, USA (1999).
-
(1999)
Linkage Learning Via Probabilistic Modeling in the ECGA
-
-
Harik, G.1
-
13
-
-
0031701602
-
Revisiting the GEMGA: Scalable evolutionary optimization through linkage learning
-
IEEE Press, Piscataway, New Jersey, USA
-
Kargupta, H.: Revisiting the GEMGA: Scalable evolutionary optimization through linkage learning. In Proceedings of 1998 IEEE International Conference on Evolutionary Computation,IEEE Press, Piscataway, New Jersey, USA (1998) 603-608.
-
(1998)
Proceedings of 1998 IEEE International Conference on Evolutionary Computation
, pp. 603-608
-
-
Kargupta, H.1
-
15
-
-
0031215849
-
The equation for response to selection and its use for prediction
-
Mühlenbein, H.: The equation for response to selection and its use for prediction. Evolutionary Computation, 5(3) (1998) 303-346.
-
(1998)
Evolutionary Computation
, vol.5
, Issue.3
, pp. 303-346
-
-
Mühlenbein, H.1
-
18
-
-
4344623174
-
Linear and Combinatorial Optimizations by Estimation of Distribution Algorithms
-
Japan
-
Paul, T.K. and Iba, H.: Linear and Combinatorial Optimizations by Estimation of Distribution Algorithms. 9th MPS Symposium on Evolutionary Computation, IPSJ Symposium 2003,Japan (2002),99-106.
-
(2002)
9th MPS Symposium on Evolutionary Computation, IPSJ Symposium
, vol.2003
, pp. 99-106
-
-
Paul, T.K.1
Iba, H.2
-
19
-
-
0034276591
-
Linkage Problem, Distribution Estimation and Bayesian Networks
-
Pelikan, M., Goldberg, D.E. and Cantú-Paz, E.: Linkage Problem, Distribution Estimation and Bayesian Networks. Evolutionary Computation, 8(3) (2000) 311-340.
-
(2000)
Evolutionary Computation
, vol.8
, Issue.3
, pp. 311-340
-
-
Pelikan, M.1
Goldberg, D.E.2
Cantú-Paz, E.3
-
20
-
-
0003654346
-
-
Technical Report, Illigal Report No. 99018, University of Illinois at Urbana-Champaign, USA
-
Pelikan, M., Goldberg, D.E. and Lobo, F.G.: A survey of optimization by building and using probabilistic models. Technical Report, Illigal Report No. 99018, University of Illinois at Urbana-Champaign, USA (1999).
-
(1999)
A Survey of Optimization by Building and Using Probabilistic Models
-
-
Pelikan, M.1
Goldberg, D.E.2
Lobo, F.G.3
-
22
-
-
0000337576
-
Simple statistical gradient-following algorithms for connectionist reinforcement learning
-
Williams, R.J.: Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learning 8 (1992) 229-256.
-
(1992)
Machine Learning
, vol.8
, pp. 229-256
-
-
Williams, R.J.1
|