Self-adaptive mechanism for multi-objective evolutionary algorithms

Proceedings of the International MultiConference of Engineers and Computer Scientists 2010, IMECS 2010

Volumn , Issue , 2010, Pages 7-12

  Zeng, Fanchao ^a   Low, Malcolm Yoke Hean ^a   Decraene, James ^a   Zhou, Suiping ^a   Cai, Wentong ^a  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

Evolutionary algorithm; Parameter tuning; Self adaptive; Simulated binary crossover

Indexed keywords

DECISION SUPPORTS; DISTRIBUTION INDEX; DYNAMIC ADJUSTMENT; ERROR-OPTIMIZATION; EVOLUTIONARY SEARCH; EXPLORATION AND EXPLOITATION; MULTI OBJECTIVE EVOLUTIONARY ALGORITHMS; MULTI-OBJECTIVE OPTIMIZATION PROBLEM; NEAR-OPTIMAL SOLUTIONS; ONLINE DECISIONS; OPTIMIZATION PROBLEMS; PARAMETER SETTING; PARAMETER-TUNING; SELF-ADAPTIVE; SIMULATED BINARY CROSSOVER;

DECISION SUPPORT SYSTEMS; ENGINEERS; MATHEMATICAL OPERATORS; MULTIOBJECTIVE OPTIMIZATION; PARAMETER ESTIMATION;

EVOLUTIONARY ALGORITHMS;

EID: 78651547321     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (11)

1. Deb, K., Agrawal, S., Pratap, A., Meyarivan, T., 2002. A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182-197. (Pubitemid 34555372)
2. Zitzler, E., Laumanns, M., Thiele, L., 2001. SPEA2: Improving the strength Pareto evolutionary algorithm. Technical Report 103, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technolgy (ETH) Zurich, Switzerland.
3. Tan, K.C., Goh, C.K., Yang, Y.J., Lee, T.H., 2006. Evolving better population distribution and exploration in evolutionary multi-objective optimization. European Journal of Operational Research 171 (2), 463-495. (Pubitemid 41706209)
4. th International Conference on Genetic Algorithms, pp. 151-157.
5. Abbass, H.A., 2002. The self-adaptive Pareto differential evolution algorithm. In: Proceedings of the 2002 Congress on Evolutionary Computation, pp. 831-836.
6. Tan, K.C., Chiam, S.C., Mamun, A.A., Goh, C.K., 2009. Balancing exploration and exploitation with adaptive variation for evolutionary multi-objective optimization. European Journal of Operational Research, 197(2), pp. 701-713.
7. Deb, K., Karthink, S., Tatsuya, O., 2007. Self-Adaptive Simulated Binary Crossover for Real-Parameter Optimization. In: Proceedings of the 9th annual conference on Genetic and evolutionary computation, 1187-1194. (Pubitemid 47291681)
8. Deb, K., Agrawal, S., 1995. Simulated binary crossover for continuous search space. Complex System, 9(2), 115-148. (Pubitemid 126034016)
9. th Asia-Pacific Conference on Simulated Evolution and Learning (SEAL'02), volume 1, pp. 13-20.
10. Laumanns, M., Thiele, L., Deb, K., Zitzler, E., 2001. On the convergence and diversity preservation properties of mulit-objective evolutionary algorithms, Technical Report 108, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technolgy (ETH) Zurich, Switzerland.
11. Deb, K., Anand, A. Joshi, D., 2002. A computationally efficient evolutionary algorithm for real-parameter optimization. Evolutionary Computation Journal, 10(4), 371-395.