Bio-inspired combinatorial optimization: Notes on reactive and proactive interaction

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

Volumn 6692 LNCS, Issue PART 2, 2011, Pages 348-355

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

^a UNIVERSITY OF MÁLAGA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

BIO-INSPIRED; CANDIDATE SOLUTION; COMBINATORIAL OPTIMIZATION PROBLEMS; COMBINATORIAL PROBLEM; EVALUATION FUNCTION; EVOLUTIONARY COMPUTING; FITNESS EVALUATIONS; FITNESS MEASURES; FUTURE DIRECTIONS; HUMAN USERS; INTERACTIVE EVOLUTIONARY ALGORITHMS; INTERACTIVE EVOLUTIONARY COMPUTATION; OPTIMAL VALUES; PROCESS OF EVOLUTION; SCHEMAS; SEARCH-BASED; PSYCHOLOGICAL FEATURES;

COMBINATORIAL OPTIMIZATION; FUNCTION EVALUATION; NEURAL NETWORKS; OPTIMIZATION; USER INTERFACES;

EVOLUTIONARY ALGORITHMS;

EID: 79957958693     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-21498-1_44     Document Type: Conference Paper

References (24)

1. Hart, W.E., Belew, R.K.: Optimizing an arbitrary function is hard for the genetic algorithm. In: Belew, R.K., Booker, L.B. (eds.) Proceedings of the 4th International Conference on Genetic Algorithms, pp. 190-195. Morgan Kaufmann, San Mateo (1991)
2. Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1(1), 67-82 (1997)
3. Bonissone, P., Subbu, R., Eklund, N., Kiehl, T.: Evolutionary Algorithms + Domain Knowledge = Real-World Evolutionary Computation. IEEE Transactions on Evolutionary Computation 10(3), 256-280 (2006)
4. 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. LNCS, vol. 3562, pp. 41-53. Springer, Heidelberg (2005)
5. 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)
6. Dawkins, R.: The BlindWatchmaker. Longman, Essex (1986)
7. Takagi, H.: Interactive evolutionary computation: Fusion of the capabilities of EC optimization and human evaluation. Proceedings of the IEEE (9), 1275-1296 (2001)
8. Lim, S., Kim, K.-M., Hong, J.-H., Cho, S.-B.: Interactive genetic programming for the sentence generation of dialogue-based travel planning system. In: 7th Asia- Pacific Conference on Complex Systems, Cairns, Australia, Asia-Pacific Workshops on Genetic Programming, pp. 6-10 (2004)
9. Lim, S., Cho, S.-B.: Language generation for conversational agent by evolution of plan trees with genetic programming. In: Torra, V., Narukawa, Y., Miyamoto, S. (eds.) MDAI 2005. LNCS (LNAI), vol. 3558, pp. 305-315. Springer, Heidelberg (2005)
10. Kosorukoff, A.: Human-based genetic algorithm. In: 2001 IEEE International Conference on Systems, Man, and Cybernetics, pp. 3464-3469. IEEE Press, Tucson (2001)
11. Breukelaar, R., Emmerich, M.T.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)
12. Kubota, N., Nojima, Y., Sulistijono, I.A., Kojima, F.: Interactive trajectory generation using evolutionary programming for a partner robot. In: 12th IEEE International Workshop on Robot and Human Interactive Communication (ROMAN 2003), Millbrae, California, USA, pp. 335-340 (2003)
13. Babbar, M., Minsker, B.: A collaborative interactive genetic algorithm framework for mixed-initiative interaction with human and simulated experts: A case study in long-term groundwater monitoring design. In: World Environmental and Water Resources Congress (2006)
14. Quiroz, J.C., Banerjee, A., Louis, S.J.: Igap: interactive genetic algorithm peer to peer. In: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, GECCO 2008, pp. 1719-1720. ACM, New York (2008)
15. Quiroz, J.C., Louis, S.J., Banerjee, A., Dascalu, S.M.: Towards creative design using collaborative interactive genetic algorithms. In: IEEE Congress on Evolutionary Computation (CEC 2009), pp. 1849-1856. IEEE, Singapore (2009)
16. Takagi, H.: Active user intervention in an ec search. In: 5th Joint Conf. Information Sciences (JCIS2000), Atlantic City, NJ, pp. 995-998 (2000)
17. Deb, K., Chaudhuri, S.: I-mode: An interactive multi-objective optimization and decision-making using evolutionary methods. KanGal report 2007003, Kanpur Genetic Algorithms Laboratory (2007)
18. Deb, K., Kumar, A.: Interactive evolutionary multi-objective optimization and decision-making using reference direction method. KanGal report 2007001, Kanpur Genetic Algorithms Laboratory (2007)
19. 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)
20. Sáez, Y., Viñuela, P.I., Segovia, J., Castro, J.C.H.: Reference chromosome to overcome user fatigue in IEC. New Generation Comput. 23(2) (2005)
21. Dozier, G.: Evolving robot behavior via interactive evolutionary computation: From real-world to simulation. In: 16th ACM Symp. Applied Computing (SAC2001), Las Vegas, NV, pp. 340-344 (2001)
22. Gong, D., Yao, X., Yuan, J.: Interactive genetic algorithms with individual fitness not assigned by human. Journal of Universal Computer Science 15(13), 2446-2462 (2009), http://www.jucs.org/jucs-15-13/interactive-genetic- algorithms-with
23. Inoue, T., Furuhashi, T., Fujii, M., Maeda, H., Takaba, M.: Development of nurse scheduling support system using interactive ea. In: IEEE Int. Conf. Systems, Man, and Cybernetics, vol. 5, pp. 533-537 (1999)
24. Klau, G., Lesh, N., Marks, J., Mitzenmacher, M.: Human-guided search. Journal of Heuristics 16, 289-310 (2010)