Overcoming hierarchical difficulty by hill-climbing the building block structure

Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference

Volumn , Issue , 2007, Pages 1256-1263

  Iclǎnzan, David ^a   Dumitrescu, Dan ^a  

^a BABE BOLYAI UNIVERSITY   (Romania)

Author keywords

Adaptive neighborhood structure; Hierarchy; Hill climbing; Linkage learning; Model building

Indexed keywords

COMPUTATIONAL EFFICIENCY; DIGITAL ARITHMETIC; HIERARCHICAL SYSTEMS; PROBLEM SOLVING;

ADAPTIVE NEIGHBORHOOD STRUCTURE; HILL CLIMBING; LINKAGE LEARNING; MODEL BUILDING;

GENETIC ALGORITHMS;

EID: 34548066598     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1276958.1277199     Document Type: Conference Paper

References (23)

1. E. D. de Jong. Representation Development from Pareto-Coevolution. In E. C.-P. et al., editor, GECCO'03: Proc. of the Genetic and Evolutionary Computation Conference, pages 262-273. Springer, LNCS, vol. 2723, July 2003.
2. E. D. de Jong, D. Thierens, and R. A. Watson. Hierarchical Genetic Algorithms. In X. Y. et al., editor, Proc. Parallel Problem Solving from Nature, pages 232-241, Birmingham, UK, 18-22 Sept. 2004. Springer, LNCS.
3. D. B. Fogel. Evolutionary Computation: Toward a New Philosophy of Machine Intelligence. IEEE Press, NY, 1995.
4. D. E. Goldberg, K. Sastry, and X. Llora;. Toward routine billion-variable optimization using genetic algorithms: Short communication. Complex., 12(3):27-29, 2007.
5. J. H. Holland. Adaptation in natural artificial systems. University of Michigan Press, Ann Arbor, 1975.
6. S. A. Kauffman. The Origins of Order. Oxford University Press, Oxford, 1993.
7. C. F. Lima, K. Sastry, D. E. Goldberg, and F. G. Lobo. Combining competent crossover and mutation operators: a probabilistic model building approach. In GECCO '05: Proceedings of the Genetic and Evolutionary Computation Conference, pages 735-742, NY, USA, 2005. ACM Press.
8. M. Mitchell, S. Forrest, and J. H. Holland. The royal road for genetic algorithms: Fitness landscapes and GA performance. In F. J. Varela, editor, Proc. of the First European Conference on Artificial Life, pages 245-254, Cambridge, MA, 1992. MIT Press.
9. H. Mühlenbein. How genetic algorithms really work: Mutation and hillclimbing. In R. Männer and B. Manderick, editors, Proc. of the Second Conference on Parallel Problem Solving from Nature (PPSN II), pages 15-25, Amsterdam, 1992. North-Holland.
10. M. Pelikan. Hierarchical Bayesian optimization algorithm: Toward a new generation of evolutionary algorithms. Springer Verlag, 2005.
11. M. Pelikan and D. E. Goldberg. Escaping hierarchical traps with competent genetic algorithms. In L. S. et al., editor, GECCO '01: Proc. of the Genetic and Evolutionary Computation Conference, pages 511-518, San Francisco, California, USA, 7-11 2001. Morgan Kaufmann.
12. K. Sastry and D. E. Goldberg. Designing competent mutation operators via probabilistic model building of neighborhoods. In GECCO '04: Proc. of the Genetic and Evolutionary Computation Conference, Seattle, WA, USA, pages 114-125. Springer, LNCS, vol. 3103, June 26-30, 2004.
13. K. Sastry and D. E. Goldberg. Let's get ready to rumble: Crossover versus mutation head to head. In GECCO '04: Proc. of the Genetic and Evolutionary Computation Conference, Seattle, WA, USA, pages 126-137. Springer, LNCS, vol. 3103, June 26-30, 2004.
14. D. Thierens and D. E. Goldberg. Mixing in genetic algorithms. In S. Forrest, editor, Proc. of the 5th International Conference on Genetic Algorithms, pages 38-47, San Francisco, CA, USA, 1993. Morgan Kaufmann.
15. Toussaint. Compact genetic codes as a search strategy of evolutionary processes. In International Workshop on Foundations of Genetic Algorithms (FOGA), volume 8. Springer, LNCS, 2005.
16. M. D. Vose. A critical examination of the schema theorem. Technical Report ut-cs-93-212, University of Tennessee, Computer Science Department, Knoxville, TN, 1993.
17. Watson, Beck, Howe, and Whitley. Problem difficulty for tabu search in job-shop scheduling. AIJ: Artificial Intelligence, 143, 2003.
18. R. A. Watson, G. Hornby, and J. B. Pollack. Modeling building-block interdependency. In PPSN V: Proc. of the 5th International Conference on Parallel Problem Solving from Nature, pages 97-108, London, UK, 1998. Springer, LNCS.
19. R. A. Watson and J. Pollack. A computational model of symbiotic composition in evolutionary transitions. Biosystems, 69(2-3):187-209, May, 2003. Special Issue on Evolvability, ed. Nehaniv.
20. R. A. Watson and J. B. Pollack. Hierarchically consistent test problems for genetic algorithms: Summary and additional results. In S. Brave, editor, GECCO '99: Late Breaking Papers at the Genetic and Evolutionary Computation Conference, pages 292-297, Orlando, Florida, USA, 13 July 1999.
21. D. Whitley, K. Mathias, S. Rana, and J. Dzubera. Building better test functions. In L. Eshelman, editor, Proc. of the Sixth International Conference on Genetic Algorithms, pages 239-246, San Francisco, CA, 1995. Morgan Kaufmann.
22. D. H. Wolpert and W. G. Macready. No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1):67-82, Apr. 1997.
23. T.-L. Yu and D. E. Goldberg. Conquering hierarchical difficulty by explicit chunking: substructural chromosome compression. In GECCO '06: Proc. of the Genetic and Evolutionary Computation Conference, pages 1385-1392, NY, USA, 2006. ACM Press.