Talk deliverd in NICSO 2010, Granada, Spain

1. 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

4. 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 ?

5. 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 

7. 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.

8. 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

9. 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

10. 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