A co-evolutionary hybrid algorithm for multi-objective optimization of gene regulatory network models

GECCO 2005 - Genetic and Evolutionary Computation Conference

Volumn , Issue , 2005, Pages 393-399

  Koduru, Praveen ^a   Das, Sanjoy ^a   Welch, Stephen ^b   Roe, Judith L ^b   Lopez Dee, Zenaida P ^b  

^a 129 Seaton Hall   (United States)

^b KANSAS STATE UNIVERSITY   (United States)

Author keywords

Genomics; Hybrid; Multi objective; Simplex

Indexed keywords

GENOMICS; MULTI-OBJECTIVE; MULTI-OBJECTIVE GENETIC ALGORITHM;

FUZZY SETS; OPTIMIZATION;

GENETIC ALGORITHMS;

EID: 32444441957     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1068009.1068073     Document Type: Conference Paper

References (19)

1. http://ergo.integratedgenomics.com/GOLD/
2. Cooper, M., Chapman, S.C., Podlich, D.W. & Hammer, G.L. In Silico Biol. 2 (2002), 151-164.
3. Welch, S.M., Roe, J.L., and Dong, Z.: A genetic neural network model of flowering time control in Arabidopsis thaliana. Agron. J. Vol 95 (2003) 71-81.
4. th International Crop Science Congress, Brisbane, Au. Sep 26 -Oct 1, 2004a. (www.cropscience.org.au/icsc2004/symposia/3/2/index.htm#TopOfPage).
5. Dong, Z. Incorporation of genomic information into the simulation of flowering time in Arabidopsis thaliana. Ph.D. dissertation, Kansas State University (2003).
6. Welch, S.M., Roe, J.L., Das, S., Dong, Z., R. He, M.B. Kirkham.: Merging genomic control networks with soil-plant-atmosphere-continuum (SPAC) models. Agricultural Systems (2004) (In press).
7. P. Koduru, S. Das, S. M. Welch, J. L. Roe, "Fuzzy Dominance Based Multi-objective GA-Simplex Hybrid Algorithms Applied to Gene Network Models", Lecture Notes in Computer Science: Proceedings of the Genetic and Evolutionary Computing Conference, Seattle, Washington, 2004.
8. Kojima, S., Y Takahashi, Y. Kobayashi, L. Monna, T. Sasaki, T. Araaki, and M. Yano. 2002. Hd3a, a rice ortholog of the Arabidopsis FT gene, promotes transition to flowering downstream of Hdl under short-day conditions. Plant Cell Physiol. 43:1096-1105.
9. Fonseca, C.M., Fleming, P.J.: An overview of evolutionary algorithms in multiobjective optimization, Evolutionary Computation, Vol. 3, no. 1 (1995) 1-16.
10. Coello Coello, C.A.: A comprehensive survey of evolutionary-based multiobjective optimization techniques, Knowledge and Information Systems, Vol. 1, no. 3 (Aug 1999) 269-308.
11. Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C.M., da Fonseca, V.G.: Performance assessment of multiobjective optimizers: An analysis and review, IEEE Transactions on Evolutionary Computation, Vol. 7, no. 2 (Apr. 2003) 117-132.
12. Zitzler, E., Thiele, L.: Multiobjective Evolutionary Algorithms: A comparative case study and the strength Pareto approach, IEEE Transactions on Evolutionary Computation, Vol. 3, no. 4 (Nov. 1999) 257-271.
13. 7. Deb, K., Agrawal,S., Pratap, A. and Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2): 182-197 (2002).
14. Mendel, J.M.: Fuzzy logic systems for engineering: A tutorial, Proceedings of the IEEE, Vol 83, No. 3 (March 1995) 345-377.
15. Renders, J.M., Flasse, S.P.: Hybrid methods using genetic algorithms for global optimization, IEEE Transactions on Systems, Man and Cybernetics Part-B, Vol. 28, no. 2 (Apr. 1998) 73-91.
16. Yen, J., Liao, J.C., Lee, B., Randolph, D.: A hybrid approach to modeling metabolic systems using a genetic algorithm and simplex method, IEEE Transactions on Systems, Man and Cybernetics Part-B, Vol. 7, no. 1 (Feb. 2003) 243-258.
17. Robin, F.; Orzati, A.; Moreno, E.; Homan, O.J.; Bachtold, W.: "Simulation and evolutionary optimization of electronbeam lithography with genetic and simplex-downhill algorithms", IEEE Transactions on Evolutionary Computation, pp. 69-82, Vol. 7, no. 1, Feb. 2003.
18. Metrics for quality assessment of a multiobjective design optimization solution set, Jin Wu, Shapour Azarm, Transactions of ASME, Vol. 123, March, 2001, pp- 18-25.
19. S. Mostaghim and J.Teich, Strategies for finding good local guides in Multi-objective particle swarm optimization. In IEEE Swarm Intelligence Symposium, Pages 26-33, 2003.