User-centric optimization with evolutionary and memetic systems

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7116 LNCS, Issue , 2012, Pages 214-221

  Espinar, Javier ^a   Cotta, Carlos ^a   Fernández Leiva, Antonio J ^a  

^a UNIVERSITY OF MÁLAGA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

APPLICATION EXAMPLES; COMBINATORIAL PROBLEM; GOLOMB RULER; INTERACTIVE OPTIMIZATION; MEMETIC; META HEURISTICS; OPTIMIZATION ALGORITHMS; OPTIMIZATION TOOLS; QUALITY OF SOLUTION; USER BEHAVIORS; USER-CENTRIC;

ALGORITHMS; OPTIMIZATION;

BEHAVIORAL RESEARCH;

EID: 84861721808     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-29843-1_24     Document Type: Conference Paper

References (12)

1. Babcock, W.C.: Intermodulation interference in radio systems. Bell Systems Technical Journal, 63-73 (1953)
2. Breukelaar, R., Emmerich, M., Bäck, T.: On Interactive Evolution Strategies. In: Rothlauf, F., Branke, J., Cagnoni, S., Costa, E., Cotta, C., Drechsler, R., Lutton, E., Machado, P., Moore, J.H., Romero, J., Smith, G.D., Squillero, G., Takagi, H. (eds.) EvoWorkshops 2006. LNCS, vol. 3907, pp. 530-541. Springer, Heidelberg (2006)
3. Cotta, C., Fernández, A.: A Hybrid GRASP - Evolutionary Algorithm Approach to Golomb Ruler Search. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo- Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 481-490. Springer, Heidelberg (2004)
4. Cotta, C., Dotú, I., Fernández, A.J., Hentenryck, P.V.: Local search-based hybrid algorithms for finding Golomb rulers. Constraints 12(3), 263-291 (2007)
5. Cotta, C., Fernández-Leiva, A.J.: Bio-inspired Combinatorial Optimization: Notes on Reactive and Proactive Interaction. In: Cabestany, J., Rojas, I., Joya, G. (eds.) IWANN 2011, Part II. LNCS, vol. 6692, pp. 348-355. Springer, Heidelberg (2011)
6. Eiben, A.E., Smith, J.E.: Introduction to evolutionary computation. Springer, Heidelberg (2003)
7. Hart, W.E., Belew, R.K.: Optimizing an arbitrary function is hard for the genetic algorithm. In: Belew, R.K., Booker, L.B. (eds.) 4th International Conference on Genetic Algorithms, pp. 190-195. Morgan Kaufmann, San Mateo CA (1991)
8. Houck, C., Joines, J., Kay, M., Wilson, J.: Empirical investigation of the benefits of partial Lamarckianism. Evolutionary Computation 5(1), 31-60 (1997)
9. Moscato, P., Cotta, C.: A modern introduction to memetic algorithms. In: Gendreau, M., Potvin, J.-Y. (eds.) Handbook of Metaheuristics, 2nd edn. International Series in Operations Research and Management Science, vol. 146, pp. 141-183. Springer, Heidelberg (2010)
10. Ohsaki, M., Takagi, H., Ohya, K.: An input method using discrete fitness values for interactive GA. Journal of Intelligent and Fuzzy Systems 6(1), 131-145 (1998)
11. Puchinger, J., Raidl, G.R.: Combining Metaheuristics and Exact Algorithms in Combinatorial Optimization: A Survey and Classification. In: Mira, J., Álvarez, J.R. (eds.) IWINAC 2005, Part II. LNCS, vol. 3562, pp. 41-53. Springer, Heidelberg (2005) (Pubitemid 41425240)
12. Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1(1), 67-82 (1997)