Hybrid evolutionary algorithms and clustering search

Studies in Computational Intelligence

Volumn 75, Issue , 2007, Pages 77-99

  Oliveira, A C M ^a   Lorena, L A N ^a  

^a NONE

Author keywords

Combinatorial and continuous optimization; Hybrid search metaheuristics

Indexed keywords

EID: 34548354655     PISSN: 1860949X     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-540-73297-6_4     Document Type: Article

References (34)

1. Jelasity M, Ortigosa P, García I (2001) UEGO, an Abstract Clustering Technique for Multimodal Global Optimization, Journal of Heuristics 7(3):215-233
2. Ray T, Liew K (2001) Society and civilization: An Optimization Algorithm Based on the Simulation of Social Behavior, IEEE Transaction on Evolutionary Computation 7(4): 386-396
3. Hart W E, Rosin C R, Belew R K, Morris G M (2000) Improved Evolutionary Hybrids for Flexible Ligand Docking in AutoDock, Optimization in Computational Chemistry and Molecular Biology 209-230
4. Moscato P (1999) Memetic Algorithms: A Short Introduction, In: Corne D, Dorigo M, Glover F (eds) New Ideas in Optimization. McGraw-Hill, London 219-234
5. Lozano M, Herrera F, Krasnogor N, Molina D (2004) Real-coded memetic algorithms with crossover hill-climbing, Evolutionary Computation 12(3):273-302
6. Yen J, Lee B (1997) A Simplex Genetic Algorithm Hybrid, IEEE International Conference on Evolutionary Computation ICEC97 175-180
7. Nelder J A, Mead R (1956) A Simplex Method for Function Minimization, Computer Journal 7(23):308-313
8. Chelouah R, Siarry P (2003) Genetic and Nelder-Mead Algorithms Hybridized for a More Accurate Global Optimization of Continuous Multiminima Functions, European Journal of Operational Research 148(2):335-348
9. Glover F (1998) A Template for Scatter Search and Path Relinking. Selected Papers from the Third European Conference on Artificial Evolution. Springer, Berlin Heidelberg New York 3-54
10. Oliveira A C M (2004) Algoritmos Evolutivos Híbridos com Detecção de Regiões Promissoras em Espaços de Busca Contínuos e Discretos. PhD Thesis. Instituto Nacional de Pesquisas Espaciais INPE São José dos Campos Brasil
11. Oliveira A C M, Lorena L A N (2004) Detecting Promising Areas by Evolutionary Clustering Search, In: Brazilian Symposium on Artificial intelligence SBIA2004 Springer, Berlin Heidelberg New York LNAI 3171:385-394
12. Chaves A A, Lorena L A N (2005) Hybrid Algorithms with Detection of Promising Areas for the Prize Collecting Travelling Salesman Problem, In: International Conference on Hybrid Intelligent Systems HIS2005 49-54
13. Feo T, Resende M (1995) Greedy Randomized Adaptive Search Procedures, Journal of Global Optimization 6:109-133
14. Mladenovic N, Hansen P (1997) Variable Neighborhood Search, Computers and Operations Research 24:1097-1100
15. Yager R R (1990) A Model of Participatory Learning, IEEE Transaction on Systems, Man and Cybernetics 20(5):1229-1234
16. Silva L R S (2003) Aprendizagem Participativa em Agrupamento Nebuloso de Dados, MA Thesis, Faculdade de Engenharia Elétrica e de Computação UNICAMP Campinas Brasil
17. Eshelman L J, Schawer J D (1993) Real-Coded Genetic Algorithms and Interval-Schemata, In: Foundation of Genetic Algorithms-2, Whitley L D (ed), Morgan Kaufmann, Los Altos, CA 187-202
18. Glover F, Laguna M, Martí R (2000) Fundamentals of Scatter Search and Path Relinking, Control and Cybernetics 39:653-684
19. Bersini H, Dorigo M, Langerman S, Seront G, Gambardella L M (1996) Results of the First International Contest on Evolutionary Optimisation 1stICEO, In: Proceedings of the IEEE-EC96 611-615
20. De Jong K A (1975) An Analysis of the Behavior of a Class of Genetic Adaptive Systems, PhD Thesis, University of Michigan Press Ann Arbor
21. Digalakis J, Margaritis K (2000) An Experimental Study of Benchmarking Functions for Genetic Algorithms, IEEE Systems Transactions 3810-3815
22. Goldberg, D E (1989) Genetic Algorithms in Search, Optimisation and Machine Learning. Addison-Wesley, Reading, MA
23. Michalewicz Z (1996) Genetic Algorithms + Data Structures = Evolution Programs. Springer, Berlin Heidelberg New York
24. Hooke R, Jeeves T A (1961) "Direct Search" Solution of Numerical and Statistical Problems, Journal of the ACM 8(2):212-229
25. Laguna M, Martí R (2002) The OptQuest Callable Library in Optimization Software Class Libraries, In. Voss S and Woodruff D L (eds), Kluwer, Boston 193-218
26. Fink A, Voss S (1999) Applications of Modern Heuristic Search Methods to Pattern Sequencing Problems, Computers and Operations Research 26(1):17-34
27. Oliveira A C M, Lorena L A N (2005) Population Training Heuristics. In: European Conference on Evolutionary Computation in Combinatorial Optimization. Springer, Berlin Heidelberg New York LNCS 3448:166-176
28. Linhares A, Yanasse H, Torreão J (1999) Linear Gate Assignment: A Fast Statistical Mechanics Approach, IEEE Transactions on Computer-Aided Designed of Integrated Circuits and Systems, 18(12):1750-1758
29. Möhring R (1990) Graph Problems Related to Gate Matrix Layout and PLA Folding, Computing 7:17-51
30. Kashiwabara T, Fujisawa T (1979) NP-Completeness of the Problem of Finding a Minimum Clique Number Interval Graph Containing a Given Graph as a Subgraph, In: Symposium of Circuits and Systems
31. Golumbic M (1980) Algorithmic Graph Theory and Perfect Graphs. Academic, New York
32. Linhares A (2002) Industrial Pattern Sequencing Problems: Some Complexity Results and New Local Search Models. PhD Thesis, Instituto Nacional de Pesquisas Espaciais INPE São José dos Campos Brasil
33. Syswerda G (1991) Schedule Optimization Using Genetic Algorithms. Handbook of Genetic Algorithms, Van Nostrand Reinhold, New York 332-349
34. Mendes A, Linhares A (2004) A Multiple Population Evolutionary Approach to Gate Matrix Layout, International Journal of Systems Science, Taylor & Francis Bristol, PA, USA 35(1):13-23