Population training heuristics

Lecture Notes in Computer Science

Volumn 3448, Issue , 2005, Pages 166-176

  Oliveira, Alexandre C M ^a   Lorena, Luiz A N ^b  

^a FEDERAL UNIVERSITY OF MARANHÃO   (Brazil)

^b INSTITUTO NACIONAL DE PESQUISAS ESPACIAIS   (Brazil)

Author keywords

GMLP; Hybrid evolutionary algorithms; MOSP; Population training

Indexed keywords

COMPUTATIONAL METHODS; EVOLUTIONARY ALGORITHMS; PATTERN RECOGNITION; PERSONNEL TRAINING; PROBLEM SOLVING;

GMLP; HYBRID EVOLUTIONARY ALGORITHMS; MOSP; POPULATION TRAINING;

HEURISTIC METHODS;

EID: 24644502715     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-31996-2_16     Document Type: Conference Paper

References (14)

1. Lorena, L.A.N., Furtado, J.C.: Constructive genetic algorithm for clustering problems. Evolutionary Computation. (2001) 9(3): 309-327.
2. Ribeiro Filho, G., Lorena, L.A.N.: A constructive evolutionary approach to school timetabling, In: Applications of Evolutionary Computing, Boers, E.J.W., Gottlieb, J., Lanzi, P.L., Smith, R.E., Cagnoni, S., Hart, E., Raidi, G.R., Tijink, H., (Eds.) - Springer LNCS 2037(2001). 130-139.
3. Oliveira, A.C.M., Lorena, L.A.N.: A constructive genetic algorithm for gate matrix layout problems. IEEE Trans. on Computer-Aided Designed of Integrated Circuits and Systems(2002) 21(8): 969-974.
4. Oliveira, A.C.M., Lorena, L.A.N.: 2-Opt population training for minimization of open stack problem. Advances in Artificial Intelligence - XVI Brazilian Symposium on Artificial Intelligence. Guilherme Bittencourt e Geber L. Ramalho (Eds). Springer LNAI 2507. (2002) 313-323.
5. Oliveira, A.C.M., Lorena, L.A.N.: Real-coded evolutionary approaches to unconstrained numerical optimization. Advances in Logic, Artificial Intelligence and Robotics. Jair Minoro Abe and João I. da Silva Filho (Eds). (2002) 10-15.
6. Reeves, C.R. Landscapes, operators and heuristic search. Annals of Operations Research, v. 86, p. 473490, 1999.
7. Fink, A., Voss, S.: Applications of modern heuristic search methods to pattern sequencing problems, Computers and Operations Research, (1999) 26(1): 17-34.
8. Golumbic, M.: Algorithmic graph theory and perfect graphs. Academic Press, New York (1980).
9. Möhring, R.: Graph problems related to gate matrix layout and PLA folding, Computing (1990) 7: 17-51.
10. Kashiwabara, T., Fujisawa, T.: NP-Completeness of the problem of finding a minimum clique number interval graph containing a given graph as a subgraph, In Proc. Symposium of Circuits and Systems(1979).
11. Linhares, A.: Industrial pattern sequencing problems: some complexity results and new local search models. Doctoral Thesis, INPE, S. José dos Campos, Brazil (2002).
12. Faggioli, E., Bentivoglio, C.A.: Heuristic and exact methods for the cutting sequencing problem, European Journal of Operational Research(1998) 110: 564-575.
13. Syswerda, G.: Schedule optimization using genetic algorithms. Handbook of Genetic Algorithms, Van Nostrand Reinhold, New York (1991) 332-349.
14. Mendes, A., Linhares, A.: A multiple population evolutionary approach to gate matrix layout, Int. Journal of Systems Science, Taylor & Francis Eds, (2004), 35(1): 13-23.