A study on large population MOEA using adaptive ε-box dominance and neighborhood recombination for many-objective optimization

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

Volumn 7219 LNCS, Issue , 2012, Pages 86-100

  Kowatari, Naoya ^a   Oyama, Akira ^b   Aguirre, Hernán E ^a   Tanaka, Kiyoshi ^a  

^a SHINSHU UNIVERSITY   (Japan)

^b JAPAN AEROSPACE EXPLORATION AGENCY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

LARGE POPULATION; MULTI OBJECTIVE EVOLUTIONARY ALGORITHMS; OPTIMIZATION PROBLEMS; PARETO OPTIMAL SOLUTIONS; PARETO-OPTIMAL SETS;

ALGORITHMS;

OPTIMIZATION;

EID: 84867867159     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-34413-8_7     Document Type: Conference Paper

References (14)

1. Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. JohnWiley & Sons, Chichester (2001)
2. Coello, C., Van Veldhuizen, D., Lamont, G.: Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer Academic Publishers, Boston (2002)
3. Aguirre, H., Tanaka, K.: Insights on Properties of Multi-objective MNK-Landscapes. In: Proc. 2004 IEEE Congress on Evolutionary Computation, pp. 196-203. IEEE Service Center (2004)
4. Aguirre, H., Tanaka, K.: Working Principles, Behavior, and Performance of MOEAs on MNK-Landscapes. European Journal of Operational Research 181(3), 1670-1690 (2007)
5. Sato, H., Aguirre, H.E., Tanaka, K.: Genetic Diversity and Effective Crossover in Evolutionary Many-objective Optimization. In: Coello, C.A.C. (ed.) LION 2011. LNCS, vol. 6683, pp. 91-105. Springer, Heidelberg (2011)
6. Ishibuchi, H., Tsukamoto, N., Nojima, Y.: Evolutionary Many-Objective Optimization: A Short Review. In: In Proc. IEEE Congress on Evolutionary Computation (CEC 2008), pp. 2424-2431. IEEE Press (2008)
7. Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable Multi-Objective Optimization Test Problems. In: Proc. 2002 Congress on Evolutionary Computation, pp. 825-830. IEEE Service Center (2002)
8. Laumanns, M., Thiele, L., Deb, K., Zitzler, E.: Combining Convergence and Diversity in Evolutionary Multi-objective Optimization. Evolutionary Computation 10(3), 263-282 (Fall 2002)
9. Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization: NSGA-II, KanGAL report 200001 (2000)
10. Aguirre, H., Tanaka, K.: Adaptive ε-Ranking on Many-Objective Problems. Evolutionary Intelligence 2(4), 183-206 (2009)
11. Purshouse, R.C., Fleming, P.J.: Evolutionary Many-Objective Optimization: An Exploratory Analysis. In: Proc. IEEE CEC 2003, pp. 2066-2073 (2003)
12. Zitzler, E.: Evolutionary Algorithms for Multiobjective Optimization:Methods and Applications. PhD thesis, Swiss Federal Institute of Technology, Zurich (1999)
13. Fonseca, C., Paquete, L., López-Ibáñez, M.: An Improved Dimension-sweep Algorithm for the Hypervolume Indicator. In: Proc. 2006 IEEE Congress on Evolutionary Computation, pp. 1157-1163. IEEE Service Center (2006)
14. Ikeda, K., Kita, H., Kobayashi, S.: Failure of Pareto-based MOEAs: does non-dominated really mean near to optimal? In: Proc. of the 2001 Congress on Evolutionary Computation, vol. 2, pp. 957-962. IEEE Service Center (2001)