A hooke-jeeves based memetic algorithm for solving dynamic optimisation problems

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

Volumn 5572 LNAI, Issue , 2009, Pages 301-309

  Moser, Irene ^a   Chiong, Raymond ^b  

^a SWINBURNE UNIVERSITY OF TECHNOLOGY   (Australia)

^b SWINBURNE UNIVERSITY OF TECHNOLOGY   (Malaysia)

Author keywords

Dynamic function optimisation; Extremal optimisation; Hooke Jeeves pattern search; Moving peaks problem

Indexed keywords

DYNAMIC FUNCTION OPTIMISATION; DYNAMIC FUNCTIONS; EXTREMAL OPTIMISATION; HOOKE-JEEVES; HOOKE-JEEVES PATTERN SEARCH; LOCAL SEARCH; MEMETIC ALGORITHMS; MOVING PEAKS PROBLEM; OPTIMISATIONS;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; EQUIPMENT TESTING;

OPTIMIZATION;

EID: 70350635352     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-02319-4_36     Document Type: Conference Paper

References (27)

1. Weise, T., Zapf, M., Chiong, R., Nebro, A.J.: Why is optimization difficult? In: Chiong, R. (ed.) Nature-Inspired Algorithms for Optimisation. Studies in Computational Intelligence, vol. 193, pp. 1-50. Springer, Heidelberg (2009)
2. Branke, J.: The moving peaks benchmark, http://www.aifb.uni-karlsruhe.de/ ~jbr/MovPeaks/ (viewed 08/11/2008)
3. Moser, I., Hendtlass, T.: A simple and efficient multi-component algorithm for solving dynamic function optimisation problems. In: Proc. IEEE Congress on Evolutionary Computation (CEC 2007), Singapore, pp. 252-259 (2007)
4. Moser, I.: Applying extremal optimisation to dynamic optimisation problems. Ph.D. thesis, Faculty of Information and Communication Technologies, Swinburne University of Technology, Australia (2008)
5. Hooke, R., Jeeves, T.: Direct search solutions of numerical and statistical problems. Journal of the Association for Computing Machinery 8, 212-229 (1961)
6. Boettcher, S., Percus, A.G.: Extremal optimization: methods derived from co-evolution. In: Proc. Genetic and Evolutionary Computation Conference (GECCO 1999), Orlando, Florida, USA, pp. 825-832 (1999)
7. Branke, J.: Memory-enhanced evolutionary algorithms for changing optimization problems. In: Proc. IEEE Congress on Evolutionary Computation (CEC 1999), Washington, DC, USA, pp. 1875-1882 (1999)
8. Branke, J., Kaußler, T., Schmidt, C., Schmeck, H.: A multi-population approach to dynamic optimization problems. In: Parmee, I.C. (ed.) Adaptive Computing in Design and Manufacturing (ACDM 2000), pp. 299-308. Springer, Berlin (2000)
9. Branke, J., Schmeck, H.: Designing evolutionary algorithms for dynamic optimization problems. In: Tsutsui, S., Ghosh, A. (eds.) Theory and Application of Evolutionary Computation: Recent Trends, pp. 239-362. Springer, Berlin (2002)
10. *CGA enabling online intrinsic. In: Proc. NASA/DoD Conference on Evolvable Hardware, Chicago, Illinois, USA, pp. 225-231 (2003)
11. Zou, X., Wang, M., Zhou, A., Mckay, B.: Evolutionary optimization based on chaotic sequence in dynamic environments. In: Proc. IEEE International Conference on Networking, Sensing and Control, Taipei, Taiwan, pp. 1364-1369 (2004)
12. Ronnewinkel, C., Martinetz, T.: Explicit speciation with few a priori parameters for dynamic optimization problems. In: GECCO Workshop on Evolutionary Algorithms for Dynamic Optimization Problems, San Francisco, California, USA, pp. 31-34 (2001)
13. Bui, L.T., Branke, J., Abbass, H.A.: Diversity as a selection pressure in dynamic environments. In: Proc. Genetic and Evolutionary Computation Conference (GECCO 2005), Washington, DC, USA, pp. 1557-1558 (2005)
14. Bui, L.T., Branke, J., Abbass, H.A.: Multiobjective optimization for dynamic environments. In: Proc. IEEE Congress on Evolutionary Computation (CEC 2005), Edinburgh, UK, pp. 2349-2356 (2005)
15. Fentress, S.W.: Exaptation as a means of evolving complex solutions. Master's Thesis, School of Informatics, University of Edinburgh, UK (2005)
16. Blackwell, T.M.: Swarms in dynamic environments. In: Proc. Genetic and Evolutionary Computation Conference (GECCO 2003), Chicago, Illinois, USA, pp. 1-12 (2003)
17. Blackwell, T., Branke, J.: Multi-swarm optimization in dynamic environments. In: Raidl, G.R., Cagnoni, S., Branke, J., Corne, D.W., Drechsler, R., Jin, Y., Johnson, C.G., Machado, P., Marchiori, E., Rothlauf, F., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2004. LNCS, vol. 3005, pp. 489-500. Springer, Heidelberg (2004)
18. Parrott, D., Li, X.: A particle swarm model for tracking multiple peaks in a dynamic environment using speciation. In: Proc. IEEE Congress on Evolutionary Computation (CEC 2004), Portland, Oregon, USA, pp. 105-116 (2004)
19. Janson, S., Middendorf, M.: A hierachical particle swarm optimizer for dynamic optimization problems. In: Raidl, G.R., Cagnoni, S., Branke, J., Corne, D.W., Drechsler, R., Jin, Y., Johnson, C.G., Machado, P., Marchiori, E., Rothlauf, F., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2004. LNCS, vol. 3005, pp. 513-524. Springer, Heidelberg (2004)
20. Wang, H., Wang, D., Yang, S.: Triggered memory-based swarm optimisation in dynamic environments. In: Giacobini, M., et al. (eds.) EvoWorkshops 2007. LNCS, vol. 4448, pp. 637-646. Springer, Heidelberg (2007)
21. Blackwell, T., Branke, J.: Multi-swarms, exclusion and anti-convergence in dynamic environments. IEEE Transactions on Evolutionary Computation 10(4), 51-58 (2006)
22. Mendes, R., Mohais, A.: DynDE: a differential evolution for dynamic optimization problems. In: Proc. IEEE Congress on Evolutionary Computation (CEC 2005), Edinburgh, UK, pp. 2808-2815 (2005)
23. Meyer, K.D., Nasut, S.J., Bishop, M.: Stochastic diffusion search: partial function evaluation in swarm intelligence dynamic optimization. In: Abraham, A., et al. (eds.) Stigmergic Optimization. Studies in Computational Intelligence, vol. 31, pp. 185-207. Springer, Berlin (2006)
24. Trojanowski, K.: B-cell algorithm as a parallel approach to optimization of moving peaks benchmark tasks. In: Proc. 6th International Conference on Computer Information Systems and Industrial Management Applications (CISIM 2007), Elk, Poland, pp. 143-148 (2007)
25. Lung, R.I., Dumitrescu, D.: A collaborative model for tracking optima in dynamic environments. In: Proc. IEEE Congress on Evolutionary Computation (CEC 2007), Singapore, pp. 564-567 (2007)
26. Bak, P., Sneppen, K.: Punctuated equilibrium and criticality in a simple model of evolution. Physics Review Letters 74, 4083-4086 (1993)
27. Boettcher, S., Percus, A.G.: Nature's way of optimizing. Artificial Intelligence 119(1), 275-286 (2000)