Variable space diversity, crossover and mutation in MOEA solving many-objective knapsack problems

Annals of Mathematics and Artificial Intelligence

Volumn 68, Issue 4, 2013, Pages 197-224

  Sato, Hiroyuki ^a   Aguirre, Hernán ^b   Tanaka, Kiyoshi ^b  

^a UNIVERSITY OF ELECTRO COMMUNICATIONS   (Japan)

^b SHINSHU UNIVERSITY   (Japan)

Author keywords

Controlling the number of crossed genes; Local recombination; Many objective 0 1 knapsack problem; Many objective optimization; Multiobjective evolutionary algorithms

Indexed keywords

EID: 84889582682     PISSN: 10122443     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10472-012-9293-y     Document Type: Article

References (33)

1. Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. John Wiley & Sons (2001).
2. Hughes, E. J.: Evolutionary many-objective optimisation: many once or one many? In: Proc. of 2005 IEEE Congress on Evolutionary Computation (CEC2005), pp. 222-227 (2005).
3. Aguirre, H., Tanaka, K.: Working principles, behavior, and performance of MOEAs on MNK-landscapes. Eur. J. Oper. Res. 181(3), 1670-1690 (2007).
4. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6, 182-197 (2002).
5. Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: improving the strength Pareto evolutionary algorithm. TIK-Report, No. 103 (2001).
6. Ishibuchi, H., Tsukamoto, N., Nojima, Y.: Evolutionary many-objective optimization: a short review. In: Proc. of 2008 IEEE Congress on Evolutionary Computation (CEC2008), pp. 2424-2431 (2008).
7. Sato, H., Aguirre, H., Tanaka, K.: Self-controlling dominance area of solutions in evolutionary many-objective optimization. In: Proc. of 8th Intl. Conf. on Simulated Evolution and Learning (SEAL2010). LNCS, vol. 6457, pp. 455-465 (2010).
8. Ishibuchi, H., Tsukamoto, N., Nojima, Y.: Iterative approach to indicator-based multiobjective optimization. In: Proc. of 2007 IEEE Congress on Evolutionary Computation (CEC2007), pp. 3697-3704 (2007).
9. Zitzler, E., Kunzili, S.: Indicator-based selection in multiobjective search. In: Proc. of 8th Intl. Conf. on Parallel Problem Solving from Nature (PPSN-VIII). LNCS, vol. 3242, pp. 832-842 (2004).
10. Kukkonen, S., Lampinen, J.: Ranking-dominance and many-objective optimization. In: Proc. of 2007 IEEE Congress on Evolutionary Computation (CEC2007), pp. 3983-3990 (2007).
11. Fabre, M., Pulido, G., Coello, C.: Alternative fitness assignment methods for many-objective optimization problems. In: Proc. of 9th International Conference Evolution Artificielle (EA2009). LNCS, vol. 5975, pp. 146-157 (2010).
12. Sato, H., Aguirre, H., Tanaka, K.: Pareto partial dominance MOEA and hybrid archiving strategy included CDAS in many-objective optimization. In: Proc. of 2010 IEEE Congress on Evolutionary Computation (CEC2010), pp. 3720-3727 (2010).
13. Ishibuchi, H., Nojima, Y.: Optimization of scalarizing functions through evolutionary multiobjective optimization. In: Proc. of 4th Intl. Conf. on Evolutionary Multi-Criterion Optimization (EMO2007), pp. 51-65 (2007).
14. Hughes, E. J.: Many-objective directed evolutionary line search. In: Proc. of 2011 Genetic and Evolutionary Computation Conference (GECCO2011), pp. 761-768. ACM Press (2011).
15. Wagner, T., Beume, N., Naujoks, B.: Pareto-, aggregation- and indicator-based methods in many-objective optimization. In: Proc. of 4th Intl. Conf. on Evolutionary Multi-Criterion Optimization (EMO2007), pp. 742-756 (2007).
16. Deb, K., Saxena, K.: Searching for Pareto-optimal solutions through dimensionality reduction for certain large-dimensional multi-objective optimization problems. In: Proc. of 2006 IEEE Congress on Evolutionary Computation (CEC2006), pp. 3353-3360 (2006).
17. Lopez Jaimes, A., Coello, C., Chakraborty, D.: Objective reduction using a feature selection technique. In: Proc. of 2008 Genetic and Evolutionary Computation Conference (GECCO2008), pp. 674-680. ACM Press (2008).
18. Brockhoff, D., Zitzler, E.: Objective reduction in evolutionary multiobjective optimization: theory and applications. Evol. Comput. 17(2), 135-166 (2009).
19. Deb, K., Sundar, J.: Preference point based multi-objective optimization using evolutionary algorithms. In: Proc. of 2006 Genetic and Evolutionary Computation Conference (GECCO2006), pp. 635-642 (2006).
20. Lopez Jaimes, A., Montano, A., Coello, C.: Preference incorporation to solve many-objective airfoil design problems. In: Proc. of 2011 IEEE Congress on Evolutionary Computation (CEC2011), pp. 1605-1612 (2011).
21. Auger, A., Bader, J., Brockhoff, D., Zitzler, E.: Articulating user preferences in many-objective problems by sampling the weighted hypervolume. In: Proc. of 2009 Genetic and Evolutionary Computation Conference (GECCO2009), pp. 555-562. ACM Press (2009).
22. Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans. Evol. Comput. 3(4), 257-271 (1999).
23. Bentley, J. L., Kung, H. T., Schkolnick, M., Thompson, C. D.: On the average number of maxima in a set of vectors and applications. J. ACM 25(4), 536-543 (1978).
24. Watanabe, S., Hiroyasu, T., Miki, M.: Neighborhood cultivation genetic algorithm for multi-objective optimization problems. In: Proc. of Genetic and Evolutionary Computation Conference (GECCO2002), pp. 458-465 (2002).
25. Sato, H., Aguirre, H., Tanaka, K.: Local dominance and local recombination in MOEAs on 0/1 multiobjective knapsack problems. Eur. J. Oper. Res. 181(3), 1670-1690 (2007).
26. Ishibuchi, H., Shibata, Y.: Mating scheme for controlling the diversity-convergence balance for multiobjective optimization. In: Proc. of 2004 Genetic and Evolutionary Computation Conference (GECCO2004), pp. 1259-1271 (2004).
27. Zhang, Q., Li, H.: MOEA/D: A multi-objective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712-731 (2007).
28. Syswerda, G.: Uniform crossover in genetic algorithms. In: Proc. of the 3rd International Conference on Genetic Algorithms (ICGA89), pp. 2-9 (1989).
29. Spears, W., De Jong, K. A.: An Analysis of Multi-Point Crossover. Foundations of Genetic Algorithms (1990).
30. Zitzler, E.: Evolutionary algorithms for multiobjective optimization: methods and applications. Ph. D. thesis, Swiss Federal Institute of Technology, Zurich (1999).
31. Kowatari, N., Oyama, A., Aguirre, H., Tanaka, K.: A study on large population MOEA using adaptive ε{lunate}-box dominance and neighborhood recombination for many-objective optimization. In: Proc. of 6th Learning and Intelligent Optimization Conference (LION6) (2012, in USB-memory).
32. Fonseca, C., Paquete, L., López-Ibáñez M.: An improved dimension-sweep algorithm for the hypervolume indicator. In: Proc. of 2006 IEEE Congress on Evolutionary Computation (CEC2006), pp. 1157-1163 (2006).
33. Sato, M., Aguirre, H., Tanaka, K.: Effects of δ-similar elimination and controlled elitism in the NSGA-II multiobjective evolutionary algorithm. In: Proc. of 2006 IEEE Congress on Evolutionary Computation (CEC2006), pp. 3980-398 (2006).