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Discovering Beneficial Cooperative Structures for the Automated Construction of Heuristics
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Discovering Beneficial Cooperative Structures for the Automated Construction of Heuristics

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Talk deliverd in NICSO 2010, Granada, Spain

Talk deliverd in NICSO 2010, Granada, Spain

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Discovering Beneficial Cooperative Structures for the Automated Construction of Heuristics Discovering Beneficial Cooperative Structures for the Automated Construction of Heuristics Presentation Transcript

  • German Terrazas [email_address] Dario Landa-Silva [email_address] Natalio Krasnogor [email_address] IV NICSO May 12 – 14, 2010 Discovering Beneficial Cooperative Structures for the Automated Construction of Heuristics Extracted from: Information Génomique et Structurale – CNRS
  • Overview
    • Hyper-heuristics
    • Heuristic design
    • Our approach
    • Proof of concept
    • Methods with results
    • Conclusions
  • Hyper-heuristics
    • Definition: search methodologies to solve hard computational problems.
    • Aim: to manufacture novel, well performing and widely applicable heuristics
    • Research directions:
      • heuristics that choose heuristics (learning assisted approaches  others’ route)
      • heuristics that generate heuristics (search & combine approaches  our route)
    • Domain-independent problem solving strategies operating on the space of heuristics (rather than the space of solutions)
  • Hyper-heuristics Search methodologies choose low-level heuristics to solve hard computational problems Space of low-level heuristics Space of solutions selects & combines 120 fast & well performing Is it possible to automatically design the correct combination of low-level heuristics, the application of which results in good solutions for a given combinatorial optimisation problem ? Combinatorial Optimisation Problem HOW TO COMBINE ?
  • Q1b: How reliable are these combinations of low-level heuristics ? Q1a: Given a set of high-level heuristics (which are combinations of low-level heuristics), is it possible to generate common combinations of low-level heuristics ? Q2: What is the performance of these combinations when applied to the validation set ? Q3a: Can pattern-based heuristics be characterised by a template ? Q3b: What is the performance of the template instances when applied to the test set ? Evaluation and filtering Randomly created heuristics Patterns identification Pattern-based heuristics construction Pattern-based created heuristics Template creation Pattern-based distilling Pattern-based Heuristics Generation Cross Validation Template-based Heuristics Distilling 1 2 3 P R O B L E M  Test dataset Validation dataset Training dataset TEMPLATE FOR 
  • Proof of Concept: STSP
    • Problem:
      • symmetric travelling salesman problem
      • 100 cities tour (kroA100)
      • Euclidean space
    • Datasets:
      • three independent datasets
      • 10 TSP tours each dataset
    • Low-level heuristics:
      • 8 TSP moves
      • deterministic and stochastic moves
    • High-level heuristics:
      • sequences of low level heuristics
      • operating in a pipeline fashion
    2X 1CI 2X
  • 10 different training tours  10 different pattern-based heuristics Q1b: How reliable are these combinations of low-level heuristics ? 1 Information sharing Beneficial local search strategies Patterns identification Pattern-based heuristic construction Evaluation and filtering Best five heuristics kroA100_0.35612 EHHGTHHGHHTGTHHDDHDH kroA100_0.43440 ADGDADTDTHDDDCDD kroA100_0.45038 DHGHGACCCHCCACADC kroA100_0.46240 GHGHHGD kroA100_0.48562 GEGHGDD kroA100_1.46475 TGFCCGC kroA100_1.66957 AAHFFAFCFFFCGTG kroA100_2.34230 TGHGHHDHDHH kroA100_2.46724 CHCCECEHFGCFCF kroA100_2.55469 ATHGAGCDT Bottom worst heuristics A C D E T F G H CHCCECEHFGCFCF GEGHGDD TGHGHHDHDHH AAHFFAFCFFFCGTG EHHGTHHGHHTGTHHDDHDH TGFCCGC GHGHHGD ATHGAGCDT ADGDADTDTHDDDCDD DHGHGACCCHCCACADC Randomly created heuristics Q1a: Given a set of high-level heuristics, is it possible to generate common combinations of low-level heuristics ? 3OPT 2OPT 2X OROPT 1CI NI AI IO Applications of 300 randomly created heuristics Applications of GDHGHHGDCDD Vs.
  • Q2: What is the performance of these combinations when applied to the validation set ? 2 For a given PBH and across the 2 nd dataset (vkroaA100 i j where i=75, j=0,…,9) 1) 300 COPIES OF PBH ( GEGHGDD ) 2) 300 RANDOMLY GENERATED HEURISTICS 3) MAX 30% SIMILARITY 4) 10 INDEP. EVALUATIONS OF 1 AND 2 300 randomly generated heuristics 300 copies of a given PBH
  • Template Q3a: Can pattern-based heuristics be characterised by a template ? 3 Common structures Building blocks C G F E G C D GA H C G FG C T TDGA D CH D D D D A CC DHH D F CD A D CTTTC C TD F CGG GD FAED HG EF CT TEE DGA D CH H D G D CHTE CC TFC D F CD ED D GGTFF C T F AHA G A D F H T G ED CT GH DGA HGH CHD E D GAF CC HC DCD EAH D TADD C DF F G G H D TGFF HG H CTD E GA EFE CH E D D D TE C A C D DC T D HFTE D FTCG CF TG GD T HG H CT CF D H GA A CH T DD ED CC DHC D T CD DDC D FD C EEH F AT GD GDAE H E G HHGD CT GFG DGA EEAH CHD H D A C E C A D C C T D TTDGF D H C E F A GD AA HGCT AE DGA TTA CHD C D HE C D CDCD CE D FEG Distilled Heuristics
  • Q3b: What is the performance of the template instances when applied to the test dataset ? Across the 3 rd dataset (gkroaA100 i j where i=75, j=0,…,9) 1) 300 GRAMMAR GENERATED HEURISTICS 2) 300 RANDOMLY GENERATED HEURISTICS 3) MAX 30% SIMILARITY 4) 10 INDEP. EVALUATIONS OF 1 AND 2 300 randomly generated heuristics 300 grammar generated heuristics
  • Conclusions
    • A general and systematic methodology for automated design of heuristics:
      • Pattern-based Heuristics Generation
      • Cross Validation
      • Template-based Heuristics Distilling
    • Identifies:
      • information sharing mechanisms (recurrent structures)
      • beneficial local search strategies (recurrent structures)
    • There are reliable patterns of low-level heuristics
    • Pattern-based heuristics are high performing (comparing to random)
    • It is possible to characterise pattern-based heuristics with a template
    • Template-based heuristics are high performing (comparing to random)
    • Building blocks are needed to guide search across space of solutions
    • Random LLH contribute with alternative paths for exploring space of solutions
    •  Building blocks + Random LLH are orchestrated into instance of a template
    • Fine tune the MSA input parameters
    • Robust across other problems
    • Thank you.
    • EPSRC grant EP/D061571/1 Next Generation Decision Support: Automating the Heuristic Design Process
    • [1] Grammatical Rules for the Automated Construction of Heuristics . G. Terrazas and N. Krasnogor. In IEEE Congress on Evolutionary Computation. IEEE Press, 2010. (to appear)
    • [2] Discovering Beneficial Cooperative Structures for the Automated Construction of Heuristics . G. Terrazas, D. Landa-Silva and N. Krasnogor. In J.R. González et al. editors, Studies in Computational Intelligence, volume 284, NICSO 2010, pp. 89–100. Elsevier, 2010.
    • [3] Towards the Design of Heuristics by means of Self-Assembly , G. Terrazas, D. Landa-Silva and N. Krasnogor, 6th International Workshop on Developments in Computational Models, 2010. (to appear)
    German Terrazas [email_address] Dario Landa-Silva [email_address] Natalio Krasnogor [email_address] IV NICSO May 12 – 14, 2010