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Overcoming hierarchical difficulty by hill-climbing the building block structure

Published: 07 July 2007 Publication History

Abstract

The Building Block Hypothesis suggests that Genetic Algorithms (GAs) are well-suited for hierarchical problems, where efficient solving requires proper problem decomposition and assembly of solution from sub-solution with strong non-linear interdependencies. The paper proposes a hill-climber operating over the building block (BB) space that can efficiently address hierarchical problems. The new Building Block Hill-Climber (BBHC) uses hill-climb search experience to learn the problem structure. The neighborhood structure is adapted whenever new knowledge about the underlaying BB structure is incorporated into the search. This allows the method to climb the hierarchical structure by revealing and solving consecutively the hierarchical levels. It is expected that for fully non-deceptive hierarchical BB structures the BBHC can solve hierarchical problems in linearithmic time. Empirical results confirm that the proposed method scales almost linearly with the problem size thus clearly outperforms population based recombinative methods.

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cover image ACM Conferences
GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
July 2007
2313 pages
ISBN:9781595936974
DOI:10.1145/1276958
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 07 July 2007

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Author Tags

  1. adaptive neighborhood structure
  2. hierarchy
  3. hill-climbing
  4. linkage learning
  5. model building

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GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;
Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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  • (2016)Searching far away from the lamp-post: An agent-based modelStrategic Organization10.1177/147612701666986915:2(242-263)Online publication date: 16-Sep-2016
  • (2014)Solving building block problems using generative grammarProceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation10.1145/2576768.2598259(341-348)Online publication date: 12-Jul-2014
  • (2014)Transforming Evolutionary Search into Higher-Level Evolutionary Search by Capturing Problem StructureIEEE Transactions on Evolutionary Computation10.1109/TEVC.2014.234770218:5(628-642)Online publication date: Oct-2014
  • (2013)Effects of discrete hill climbing on model building forestimation of distribution algorithmsProceedings of the 15th annual conference on Genetic and evolutionary computation10.1145/2463372.2463418(367-374)Online publication date: 6-Jul-2013
  • (2011)Parameter-less local optimizer with linkage identification for deterministic order-k decomposable problemsProceedings of the 13th annual conference on Genetic and evolutionary computation10.1145/2001576.2001656(577-584)Online publication date: 12-Jul-2011
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