Automatically improving the anytime behaviour of optimisation algorithms

•A method to automatically improve the anytime behaviour of optimisation algorithms.•Anytime behaviour is evaluated by the hypervolume measure.•Decision-maker’s preferences may be incorporated into the automatic tuning procedure.•Case-studies include configuring a heuristic algorithm and an MIP solv...

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Published in:	European journal of operational research Vol. 235; no. 3; pp. 569 - 582
Main Authors:	López-Ibáñez, Manuel, Stützle, Thomas
Format:	Journal Article
Language:	English
Published:	Amsterdam Elsevier B.V 16.06.2014 Elsevier Sequoia S.A
Subjects:	Algorithms Anytime algorithms Automatic configuration Automation Computation Integer programming Mathematical models Metaheuristics Offline tuning Optimization Optimization algorithms Source code Studies Tradeoff analysis Tradeoffs Tuning Anytime algorithms Metaheuristics Automatic configuration Offline tuning
ISSN:	0377-2217, 1872-6860
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Abstract	•A method to automatically improve the anytime behaviour of optimisation algorithms.•Anytime behaviour is evaluated by the hypervolume measure.•Decision-maker’s preferences may be incorporated into the automatic tuning procedure.•Case-studies include configuring a heuristic algorithm and an MIP solver. Optimisation algorithms with good anytime behaviour try to return as high-quality solutions as possible independently of the computation time allowed. Designing algorithms with good anytime behaviour is a difficult task, because performance is often evaluated subjectively, by plotting the trade-off curve between computation time and solution quality. Yet, the trade-off curve may be modelled also as a set of mutually nondominated, bi-objective points. Using this model, we propose to combine an automatic configuration tool and the hypervolume measure, which assigns a single quality measure to a nondominated set. This allows us to improve the anytime behaviour of optimisation algorithms by means of automatically finding algorithmic configurations that produce the best nondominated sets. Moreover, the recently proposed weighted hypervolume measure is used here to incorporate the decision-maker’s preferences into the automatic tuning procedure. We report on the improvements reached when applying the proposed method to two relevant scenarios: (i) the design of parameter variation strategies for MAX-MIN Ant System and (ii) the tuning of the anytime behaviour of SCIP, an open-source mixed integer programming solver with more than 200 parameters.
AbstractList	Optimisation algorithms with good anytime behaviour try to return as high-quality solutions as possible independently of the computation time allowed. Designing algorithms with good anytime behaviour is a difficult task, because performance is often evaluated subjectively, by plotting the trade-off curve between computation time and solution quality. Yet, the trade-off curve may be modelled also as a set of mutually nondominated, bi-objective points. Using this model, we propose to combine an automatic configuration tool and the hypervolume measure, which assigns a single quality measure to a nondominated set. This allows us to improve the anytime behaviour of optimisation algorithms by means of automatically finding algorithmic configurations that produce the best nondominated sets. Moreover, the recently proposed weighted hypervolume measure is used here to incorporate the decision-maker's preferences into the automatic tuning procedure. We report on the improvements reached when applying the proposed method to two relevant scenarios: (i) the design of parameter variation strategies for MAX-MIN Ant System and (ii) the tuning of the anytime behaviour of SCIP, an open-source mixed integer programming solver with more than 200 parameters. •A method to automatically improve the anytime behaviour of optimisation algorithms.•Anytime behaviour is evaluated by the hypervolume measure.•Decision-maker’s preferences may be incorporated into the automatic tuning procedure.•Case-studies include configuring a heuristic algorithm and an MIP solver. Optimisation algorithms with good anytime behaviour try to return as high-quality solutions as possible independently of the computation time allowed. Designing algorithms with good anytime behaviour is a difficult task, because performance is often evaluated subjectively, by plotting the trade-off curve between computation time and solution quality. Yet, the trade-off curve may be modelled also as a set of mutually nondominated, bi-objective points. Using this model, we propose to combine an automatic configuration tool and the hypervolume measure, which assigns a single quality measure to a nondominated set. This allows us to improve the anytime behaviour of optimisation algorithms by means of automatically finding algorithmic configurations that produce the best nondominated sets. Moreover, the recently proposed weighted hypervolume measure is used here to incorporate the decision-maker’s preferences into the automatic tuning procedure. We report on the improvements reached when applying the proposed method to two relevant scenarios: (i) the design of parameter variation strategies for MAX-MIN Ant System and (ii) the tuning of the anytime behaviour of SCIP, an open-source mixed integer programming solver with more than 200 parameters. Optimisation algorithms with good anytime behaviour try to return as high-quality solutions as possible independently of the computation time allowed. Designing algorithms with good anytime behaviour is a difficult task, because performance is often evaluated subjectively, by plotting the trade-off curve between computation time and solution quality. Yet, the trade-off curve may be modelled also as a set of mutually nondominated, bi-objective points. Using this model, we propose to combine an automatic configuration tool and the hypervolume measure, which assigns a single quality measure to a nondominated set. This allows us to improve the anytime behaviour of optimisation algorithms by means of automatically finding algorithmic configurations that produce the best nondominated sets. Moreover, the recently proposed weighted hypervolume measure is used here to incorporate the decision-maker's preferences into the automatic tuning procedure. We report on the improvements reached when applying the proposed method to two relevant scenarios: (i) the design of parameter variation strategies for MAX-MIN Ant System and (ii) the tuning of the anytime behaviour of SCIP, an open-source mixed integer programming solver with more than 200 parameters. [PUBLICATION ABSTRACT]
Author	López-Ibáñez, Manuel Stützle, Thomas
Author_xml	– sequence: 1 givenname: Manuel surname: López-Ibáñez fullname: López-Ibáñez, Manuel email: manuel.lopez-ibanez@ulb.ac.be – sequence: 2 givenname: Thomas surname: Stützle fullname: Stützle, Thomas email: stuetzle@ulb.ac.be
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Cites_doi	10.1145/2076450.2076469 10.1613/jair.2861 10.1109/TEVC.2011.2182651 10.1016/j.ejor.2006.12.062 10.1287/opre.1050.0243 10.1109/4235.797969 10.1007/s12532-008-0001-1 10.1109/TEVC.2003.810758 10.1016/S0167-739X(00)00043-1 10.1023/A:1026569813391 10.1016/j.tcs.2011.03.012 10.1109/4235.585892 10.1109/TEVC.2009.2021465 10.1504/IJMHEUR.2011.041195 10.1023/A:1006556606079 10.1016/j.swevo.2011.02.001 10.1016/j.asoc.2008.07.001
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References	López-Ibáñez, Stützle (b0220) 2012; 160 Auger, Hansen (b0035) 2005 Gagliolo, Legrand (b0135) 2010 Technical Report TR/IRIDIA/2012-012, IRIDIA, Université Libre de Bruxelles, Belgium. Wessing, Beume, Rudolph, Naujoks (b0280) 2010; Vol. 6238 Auger, Bader, Brockhoff, Zitzler (b0030) 2012; 425 Dorigo, Stützle (b0105) 2004 Stützle T. (2002). ACOTSP: A software package of various ant colony optimization algorithms applied to the symmetric traveling salesman problem. Bartz-Beielstein (b0045) 2006 Eiben, Smit (b0125) 2011; 10 Conover (b0080) 1999 Zitzler, Brockhoff, Thiele (b0295) 2007; Vol. 4403 Nannen, Eiben (b0245) 2006 Zilberstein (b0290) 1996; 170 PhD thesis, FG Intellektik, FB Informatik, TU Darmstadt, Germany. Hutter, Hoos, Leyton-Brown, Stützle (b0160) 2009; 36 Ansótegui, Sellmann, Tierney (b0020) 2009; Vol. 5732 Knowles, J. D., Thiele, L., & Zitzler, E. (2006). Hutter, Hoos, Leyton-Brown (b0155) 2011 KhudaBukhsh, Xu, Hoos, Leyton-Brown (b0170) 2009 Montes de Oca, Stützle, Birattari, Dorigo (b0240) 2009; 130 Stützle, T. (1997). Birattari, Stützle, Paquete, Varrentrapp (b0060) 2002 Loudni, Boizumault (b0225) 2008; 191 Knowles, J. D. (2005). A summary-attainment-surface palotting method for visualizing the performance of stochastic multiobjective optimizers. In Hoos, Stützle (b0150) 2005 López-Ibáñez, Stützle (b0215) 2010; Vol. 6234 Technical Report AIDA-97-4, FG Intellektik, FB Informatik, TU Darmstadt, Germany, July 1997. Dean, Boddy (b0090) 1988 . López-Ibáñez, M. & Stützle, T. (2012b). Achterberg (b0005) 2009; 10 López-Ibáñez, Paquete, Stützle (b0210) 2010 Hoos (b0145) 2012; 550 Coy, Golden, Runger, Wasil (b0085) 2001; 70 Dubois-Lacoste, López-Ibáñez, Stützle (b0115) 2011 Maur, López-Ibáñez, Stützle (b0235) 2010 Aine, Kumar, Chakrabarti (b0015) 2009; 90 Adenso-Díaz, Laguna (b0010) 2006; 540 López-Ibáñez, M., Dubois-Lacoste, J., Stützle, T., & Birattari M. (2011). Technical Report TR/IRIDIA/2011-004, IRIDIA, Université Libre de Bruxelles, Belgium. Zitzler, Thiele, Laumanns, Fonseca, Grunert da Fonseca (b0305) 2003; 70 López-Ibáñez, Liao, Stützle (b0205) 2012; Vol. 7491 Wah, Chen (b0275) 2000; Vol. 1894 Woodruff, Ritzinger, Oppen (b0285) 2011; 10 Bartz-Beielstein, Lasarczyk, Preuss (b0050) 2010 PhD thesis, FB Informatik, TU Darmstadt, Germany. Eiben, Michalewicz, Schoenauer, Smith (b0120) 2007 Maron, Moore (b0230) 1997; 110 Dréo (b0110) 2009 TIK-Report 214, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH), Zürich, Switzerland, February 2006. Revised version. Zitzler, Thiele (b0300) 1999; 30 Grunert da Fonseca, Fonseca (b0140) 2010 Dorigo, Gambardella (b0100) 1997; 10 Johnson, D. S., McGeoch, L. A., Rego, C., & Glover, F. (2001). Balaprakash, Birattari, Stützle (b0040) 2007; Vol. 4771 Auger, Bader, Brockhoff, Zitzler (b0025) 2009 Birattari, Yuan, Balaprakash, Stützle (b0065) 2010 Leyton-Brown, Pearson, Shoham (b0185) 2000 López-Ibáñez, M. & Stützle, T. (2012a). Chiarandini, M. (2005). Birattari (b0055) 2009; Vol. 197 Branke, Elomari (b0070) 2011 Stützle, López-Ibáñez, Pellegrini, Maur, Montes de Oca, Birattari (b0270) 2012 Fonseca, Paquete, López-Ibáñez (b0130) 2006 Radulescu, López-Ibáñez, Stützle (b0250) 2013; Vol. 7811 Stützle, Hoos (b0265) 2000; 160 (pp. 552–557). den Besten, M. (2004). Conover (10.1016/j.ejor.2013.10.043_b0080) 1999 Achterberg (10.1016/j.ejor.2013.10.043_b0005) 2009; 10 Montes de Oca (10.1016/j.ejor.2013.10.043_b0240) 2009; 130 10.1016/j.ejor.2013.10.043_b0190 Auger (10.1016/j.ejor.2013.10.043_b0030) 2012; 425 Auger (10.1016/j.ejor.2013.10.043_b0025) 2009 10.1016/j.ejor.2013.10.043_b0195 10.1016/j.ejor.2013.10.043_b0075 Stützle (10.1016/j.ejor.2013.10.043_b0270) 2012 Wessing (10.1016/j.ejor.2013.10.043_b0280) 2010; Vol. 6238 Auger (10.1016/j.ejor.2013.10.043_b0035) 2005 Hutter (10.1016/j.ejor.2013.10.043_b0155) 2011 Bartz-Beielstein (10.1016/j.ejor.2013.10.043_b0045) 2006 Birattari (10.1016/j.ejor.2013.10.043_b0065) 2010 Hoos (10.1016/j.ejor.2013.10.043_b0145) 2012; 550 Ansótegui (10.1016/j.ejor.2013.10.043_b0020) 2009; Vol. 5732 Eiben (10.1016/j.ejor.2013.10.043_b0120) 2007 10.1016/j.ejor.2013.10.043_b0180 10.1016/j.ejor.2013.10.043_b0260 Eiben (10.1016/j.ejor.2013.10.043_b0125) 2011; 10 Stützle (10.1016/j.ejor.2013.10.043_b0265) 2000; 160 López-Ibáñez (10.1016/j.ejor.2013.10.043_b0220) 2012; 160 Coy (10.1016/j.ejor.2013.10.043_b0085) 2001; 70 Zitzler (10.1016/j.ejor.2013.10.043_b0305) 2003; 70 Woodruff (10.1016/j.ejor.2013.10.043_b0285) 2011; 10 Hoos (10.1016/j.ejor.2013.10.043_b0150) 2005 Zilberstein (10.1016/j.ejor.2013.10.043_b0290) 1996; 170 KhudaBukhsh (10.1016/j.ejor.2013.10.043_b0170) 2009 López-Ibáñez (10.1016/j.ejor.2013.10.043_b0205) 2012; Vol. 7491 Bartz-Beielstein (10.1016/j.ejor.2013.10.043_b0050) 2010 Birattari (10.1016/j.ejor.2013.10.043_b0060) 2002 Fonseca (10.1016/j.ejor.2013.10.043_b0130) 2006 Adenso-Díaz (10.1016/j.ejor.2013.10.043_b0010) 2006; 540 10.1016/j.ejor.2013.10.043_b0095 Wah (10.1016/j.ejor.2013.10.043_b0275) 2000; Vol. 1894 10.1016/j.ejor.2013.10.043_b0255 Dean (10.1016/j.ejor.2013.10.043_b0090) 1988 Dorigo (10.1016/j.ejor.2013.10.043_b0100) 1997; 10 Dréo (10.1016/j.ejor.2013.10.043_b0110) 2009 Nannen (10.1016/j.ejor.2013.10.043_b0245) 2006 Birattari (10.1016/j.ejor.2013.10.043_b0055) 2009; Vol. 197 Maur (10.1016/j.ejor.2013.10.043_b0235) 2010 10.1016/j.ejor.2013.10.043_b0175 Aine (10.1016/j.ejor.2013.10.043_b0015) 2009; 90 Grunert da Fonseca (10.1016/j.ejor.2013.10.043_b0140) 2010 Balaprakash (10.1016/j.ejor.2013.10.043_b0040) 2007; Vol. 4771 Branke (10.1016/j.ejor.2013.10.043_b0070) 2011 Hutter (10.1016/j.ejor.2013.10.043_b0160) 2009; 36 Zitzler (10.1016/j.ejor.2013.10.043_b0295) 2007; Vol. 4403 Radulescu (10.1016/j.ejor.2013.10.043_b0250) 2013; Vol. 7811 Gagliolo (10.1016/j.ejor.2013.10.043_b0135) 2010 Leyton-Brown (10.1016/j.ejor.2013.10.043_b0185) 2000 10.1016/j.ejor.2013.10.043_b0200 Loudni (10.1016/j.ejor.2013.10.043_b0225) 2008; 191 10.1016/j.ejor.2013.10.043_b0165 Maron (10.1016/j.ejor.2013.10.043_b0230) 1997; 110 Dorigo (10.1016/j.ejor.2013.10.043_b0105) 2004 Zitzler (10.1016/j.ejor.2013.10.043_b0300) 1999; 30 Dubois-Lacoste (10.1016/j.ejor.2013.10.043_b0115) 2011 López-Ibáñez (10.1016/j.ejor.2013.10.043_b0215) 2010; Vol. 6234 López-Ibáñez (10.1016/j.ejor.2013.10.043_b0210) 2010
References_xml	– start-page: 11 year: 2002 end-page: 18 ident: b0060 article-title: A racing algorithm for configuring metaheuristics 2002 publication-title: Proceedings of GECCO – volume: 160 start-page: 889 year: 2000 end-page: 914 ident: b0265 article-title: MAX-MIN ant system publication-title: Future Generation Computer Systems – volume: 425 start-page: 0 75 year: 2012 end-page: 103 ident: b0030 article-title: Hypervolume-based multiobjective optimization: Theoretical foundations and practical implications publication-title: Theoretical Computer Science – volume: Vol. 4771 start-page: 108 year: 2007 end-page: 122 ident: b0040 article-title: Improvement strategies for the F-race algorithm: Sampling design and iterative refinement publication-title: Hybrid metaheuristics – start-page: 563 year: 2009 end-page: 570 ident: b0025 article-title: Investigating and exploiting the bias of the weighted hypervolume to articulate user preferences publication-title: Proceedings of GECCO 2009 – reference: Chiarandini, M. (2005). – start-page: 49 year: 1988 end-page: 54 ident: b0090 article-title: An analysis of time-dependent planning publication-title: Proceedings of the 7th national conference on artificial intelligence – year: 2011 ident: b0155 article-title: Sequential model-based optimization for general algorithm configuration publication-title: Learning and intelligent optimization, 5th international conference, LION 5 – reference: (pp. 552–557). – year: 2006 ident: b0045 article-title: Experimental research in evolutionary computation: The new experimentalism – reference: López-Ibáñez, M. & Stützle, T. (2012a). – start-page: 209 year: 2010 end-page: 222 ident: b0210 article-title: Exploratory analysis of stochastic local search algorithms in biobjective optimization publication-title: Experimental methods for the analysis of optimization algorithms – reference: Johnson, D. S., McGeoch, L. A., Rego, C., & Glover, F. (2001). – reference: . PhD thesis, FG Intellektik, FB Informatik, TU Darmstadt, Germany. – start-page: 2197 year: 2009 end-page: 2200 ident: b0110 article-title: Using performance fronts for parameter setting of stochastic metaheuristics publication-title: GECCO (companion) – reference: Knowles, J. D., Thiele, L., & Zitzler, E. (2006). – volume: 540 start-page: 0 99 year: 2006 end-page: 114 ident: b0010 article-title: Fine-tuning of algorithms using fractional experimental design and local search publication-title: Operations Research – volume: 110 start-page: 193 year: 1997 end-page: 225 ident: b0230 article-title: The racing algorithm: Model selection for lazy learners publication-title: Artificial Intelligence Research – volume: 70 start-page: 0 117 year: 2003 end-page: 132 ident: b0305 article-title: Performance assessment of multiobjective optimizers: an analysis and review publication-title: IEEE Transactions on Evolutionary Computation – volume: Vol. 7491 start-page: 357 year: 2012 end-page: 366 ident: b0205 article-title: On the anytime behavior of IPOP-CMA-ES publication-title: PPSN 2012, Part I – volume: Vol. 5732 start-page: 142 year: 2009 end-page: 157 ident: b0020 article-title: A gender-based genetic algorithm for the automatic configuration of algorithms publication-title: Principles and practice of constraint programming, CP 2009 – volume: 550 start-page: 0 70 year: 2012 end-page: 80 ident: b0145 article-title: Programming by optimization publication-title: Communications of the ACM – reference: López-Ibáñez, M. & Stützle, T. (2012b). – volume: 10 start-page: 0 53 year: 1997 end-page: 66 ident: b0100 article-title: Ant colony system: A cooperative learning approach to the traveling salesman problem publication-title: IEEE Transactions on Evolutionary Computation – start-page: 66 year: 2000 end-page: 76 ident: b0185 article-title: Towards a universal test suite for combinatorial auction algorithms publication-title: ACM conference on electronic commerce (EC-00) – reference: . Technical Report TR/IRIDIA/2011-004, IRIDIA, Université Libre de Bruxelles, Belgium. – volume: Vol. 6234 start-page: 95 year: 2010 end-page: 106 ident: b0215 article-title: Automatic configuration of multi-objective ACO algorithms publication-title: Swarm intelligence, 7th international conference, ANTS 2010 – volume: 170 start-page: 0 73 year: 1996 end-page: 83 ident: b0290 article-title: Using anytime algorithms in intelligent systems publication-title: AI Magazine – volume: 10 start-page: 0 1 year: 2009 end-page: 41 ident: b0005 article-title: SCIP: Solving constraint integer programs publication-title: Mathematical Programming Computation – start-page: 103 year: 2010 end-page: 130 ident: b0140 article-title: The attainment-function approach to stochastic multiobjective optimizer assessment and comparison publication-title: Experimental methods for the analysis of optimization algorithms – reference: . – reference: den Besten, M. (2004). – start-page: 311 year: 2010 end-page: 336 ident: b0065 article-title: F-race and iterated F-race: An overview publication-title: Experimental methods for the analysis of optimization algorithms – volume: 160 start-page: 0 861 year: 2012 end-page: 875 ident: b0220 article-title: The automatic design of multi-objective ant colony optimization algorithms publication-title: IEEE Transactions on Evolutionary Computation – reference: . TIK-Report 214, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH), Zürich, Switzerland, February 2006. Revised version. – start-page: 1157 year: 2006 end-page: 1163 ident: b0130 article-title: An improved dimension-sweep algorithm for the hypervolume indicator publication-title: Proceedings of the 2006 congress on evolutionary computation (CEC 2006) – start-page: 191 year: 2012 end-page: 215 ident: b0270 article-title: Parameter adaptation in ant colony optimization publication-title: Autonomous search – volume: 70 start-page: 77 year: 2001 end-page: 97 ident: b0085 article-title: Using experimental design to find effective parameter settings for heuristics publication-title: Journal of Heuristics – volume: 30 start-page: 0 257 year: 1999 end-page: 271 ident: b0300 article-title: Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto evolutionary algorithm publication-title: IEEE Transactions on Evolutionary Computation – volume: 90 start-page: 0 527 year: 2009 end-page: 540 ident: b0015 article-title: Adaptive parameter control of evolutionary algorithms to improve quality-time trade-off publication-title: Applied Soft Computing – start-page: 3823 year: 2010 end-page: 3830 ident: b0235 article-title: Pre-scheduled and adaptive parameter variation in MAX-MIN Ant System publication-title: Proceedings of the 2010 congress on evolutionary computation (CEC 2010) – reference: . PhD thesis, FB Informatik, TU Darmstadt, Germany. – volume: 10 start-page: 19 year: 2011 end-page: 31 ident: b0125 article-title: Parameter tuning for configuring and analyzing evolutionary algorithms publication-title: Swarm and Evolutionary Computation – volume: 130 start-page: 1120 year: 2009 end-page: 1132 ident: b0240 article-title: Frankenstein’s PSO: A composite particle swarm optimization algorithm publication-title: IEEE Transactions on Evolutionary Computation – volume: 191 start-page: 705 year: 2008 end-page: 735 ident: b0225 article-title: Combining VNS with constraint programming for solving anytime optimization problems publication-title: European Journal of Operational Research – volume: Vol. 1894 start-page: 425 year: 2000 end-page: 440 ident: b0275 article-title: Optimal anytime constrained simulated annealing for constrained global optimization publication-title: Principles and practice of constraint programming, CP 2000 – reference: López-Ibáñez, M., Dubois-Lacoste, J., Stützle, T., & Birattari M. (2011). – start-page: 263 year: 2011 end-page: 264 ident: b0070 article-title: Simultaneous tuning of metaheuristic parameters for various computing budgets publication-title: Proceedings of GECCO 2011 – reference: Knowles, J. D. (2005). A summary-attainment-surface palotting method for visualizing the performance of stochastic multiobjective optimizers. In – volume: Vol. 7811 start-page: 825 year: 2013 end-page: 840 ident: b0250 article-title: Automatically improving the anytime behaviour of multiobjective evolutionary algorithms publication-title: EMO 2013 – start-page: 1769 year: 2005 end-page: 1776 ident: b0035 article-title: A restart CMA evolution strategy with increasing population size publication-title: Proceedings of CEC 2005 – volume: Vol. 4403 start-page: 862 year: 2007 end-page: 876 ident: b0295 article-title: The hypervolume indicator revisited: On the design of Pareto-compliant indicators via weighted integration publication-title: EMO 2007 – start-page: 517 year: 2009 end-page: 524 ident: b0170 article-title: SATenstein: Automatically building local search SAT solvers from components publication-title: Proceedings of the twenty-first international joint conference on artificial intelligence (IJCAI-09) – start-page: 19 year: 2007 end-page: 46 ident: b0120 article-title: Parameter control in evolutionary algorithms publication-title: Parameter setting in evolutionary algorithms – start-page: 2019 year: 2011 end-page: 2026 ident: b0115 article-title: Automatic configuration of state-of-the-art multi-objective optimizers using the TP publication-title: Proceedings of GECCO 2011 – start-page: 183 year: 2006 end-page: 190 ident: b0245 article-title: A method for parameter calibration and relevance estimation in evolutionary algorithms publication-title: Proceedings of GECCO 2006 – reference: Stützle T. (2002). ACOTSP: A software package of various ant colony optimization algorithms applied to the symmetric traveling salesman problem. – volume: 10 start-page: 0 222 year: 2011 end-page: 231 ident: b0285 article-title: Research note: the point of diminishing returns in heuristic search publication-title: International Journal of Metaheuristics – year: 1999 ident: b0080 article-title: Practical nonparametric statistics – volume: 36 start-page: 267 year: 2009 end-page: 306 ident: b0160 article-title: ParamILS: An automatic algorithm configuration framework publication-title: Journal of Artificial Intelligence Research – volume: Vol. 197 year: 2009 ident: b0055 article-title: Tuning metaheuristics: A machine learning perspective publication-title: Studies in computational intelligence – reference: . – start-page: 337 year: 2010 end-page: 360 ident: b0050 article-title: The sequential parameter optimization toolbox publication-title: Experimental methods for the analysis of optimization algorithms – volume: Vol. 6238 start-page: 728 year: 2010 end-page: 737 ident: b0280 article-title: Parameter tuning boosts performance of variation operators in multiobjective optimization publication-title: Parallel problem solving from nature XI – year: 2005 ident: b0150 article-title: Stochastic local search—Foundations and applications – reference: Stützle, T. (1997). – reference: . Technical Report TR/IRIDIA/2012-012, IRIDIA, Université Libre de Bruxelles, Belgium. – start-page: 161 year: 2010 end-page: 184 ident: b0135 article-title: Algorithm survival analysis publication-title: Experimental methods for the analysis of optimization algorithms – year: 2004 ident: b0105 article-title: Ant colony optimization – reference: . Technical Report AIDA-97-4, FG Intellektik, FB Informatik, TU Darmstadt, Germany, July 1997. – volume: Vol. 197 year: 2009 ident: 10.1016/j.ejor.2013.10.043_b0055 article-title: Tuning metaheuristics: A machine learning perspective – volume: Vol. 7811 start-page: 825 year: 2013 ident: 10.1016/j.ejor.2013.10.043_b0250 article-title: Automatically improving the anytime behaviour of multiobjective evolutionary algorithms – year: 2006 ident: 10.1016/j.ejor.2013.10.043_b0045 – volume: 550 start-page: 0 70 issue: 2 year: 2012 ident: 10.1016/j.ejor.2013.10.043_b0145 article-title: Programming by optimization publication-title: Communications of the ACM doi: 10.1145/2076450.2076469 – volume: 36 start-page: 267 year: 2009 ident: 10.1016/j.ejor.2013.10.043_b0160 article-title: ParamILS: An automatic algorithm configuration framework publication-title: Journal of Artificial Intelligence Research doi: 10.1613/jair.2861 – volume: 160 start-page: 0 861 issue: 6 year: 2012 ident: 10.1016/j.ejor.2013.10.043_b0220 article-title: The automatic design of multi-objective ant colony optimization algorithms publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2011.2182651 – ident: 10.1016/j.ejor.2013.10.043_b0255 – volume: 191 start-page: 705 year: 2008 ident: 10.1016/j.ejor.2013.10.043_b0225 article-title: Combining VNS with constraint programming for solving anytime optimization problems publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2006.12.062 – volume: 540 start-page: 0 99 issue: 1 year: 2006 ident: 10.1016/j.ejor.2013.10.043_b0010 article-title: Fine-tuning of algorithms using fractional experimental design and local search publication-title: Operations Research doi: 10.1287/opre.1050.0243 – ident: 10.1016/j.ejor.2013.10.043_b0190 – volume: 30 start-page: 0 257 issue: 4 year: 1999 ident: 10.1016/j.ejor.2013.10.043_b0300 article-title: Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto evolutionary algorithm publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/4235.797969 – ident: 10.1016/j.ejor.2013.10.043_b0175 – ident: 10.1016/j.ejor.2013.10.043_b0095 – year: 1999 ident: 10.1016/j.ejor.2013.10.043_b0080 – ident: 10.1016/j.ejor.2013.10.043_b0200 – volume: Vol. 4403 start-page: 862 year: 2007 ident: 10.1016/j.ejor.2013.10.043_b0295 article-title: The hypervolume indicator revisited: On the design of Pareto-compliant indicators via weighted integration – year: 2005 ident: 10.1016/j.ejor.2013.10.043_b0150 – volume: Vol. 1894 start-page: 425 year: 2000 ident: 10.1016/j.ejor.2013.10.043_b0275 article-title: Optimal anytime constrained simulated annealing for constrained global optimization – start-page: 337 year: 2010 ident: 10.1016/j.ejor.2013.10.043_b0050 article-title: The sequential parameter optimization toolbox – start-page: 49 year: 1988 ident: 10.1016/j.ejor.2013.10.043_b0090 article-title: An analysis of time-dependent planning – start-page: 517 year: 2009 ident: 10.1016/j.ejor.2013.10.043_b0170 article-title: SATenstein: Automatically building local search SAT solvers from components – ident: 10.1016/j.ejor.2013.10.043_b0195 – start-page: 161 year: 2010 ident: 10.1016/j.ejor.2013.10.043_b0135 article-title: Algorithm survival analysis – ident: 10.1016/j.ejor.2013.10.043_b0180 – start-page: 1157 year: 2006 ident: 10.1016/j.ejor.2013.10.043_b0130 article-title: An improved dimension-sweep algorithm for the hypervolume indicator – start-page: 563 year: 2009 ident: 10.1016/j.ejor.2013.10.043_b0025 article-title: Investigating and exploiting the bias of the weighted hypervolume to articulate user preferences – start-page: 66 year: 2000 ident: 10.1016/j.ejor.2013.10.043_b0185 article-title: Towards a universal test suite for combinatorial auction algorithms – volume: 10 start-page: 0 1 issue: 1 year: 2009 ident: 10.1016/j.ejor.2013.10.043_b0005 article-title: SCIP: Solving constraint integer programs publication-title: Mathematical Programming Computation doi: 10.1007/s12532-008-0001-1 – start-page: 1769 year: 2005 ident: 10.1016/j.ejor.2013.10.043_b0035 article-title: A restart CMA evolution strategy with increasing population size – start-page: 2197 year: 2009 ident: 10.1016/j.ejor.2013.10.043_b0110 article-title: Using performance fronts for parameter setting of stochastic metaheuristics – start-page: 103 year: 2010 ident: 10.1016/j.ejor.2013.10.043_b0140 article-title: The attainment-function approach to stochastic multiobjective optimizer assessment and comparison – ident: 10.1016/j.ejor.2013.10.043_b0075 – volume: 70 start-page: 0 117 issue: 2 year: 2003 ident: 10.1016/j.ejor.2013.10.043_b0305 article-title: Performance assessment of multiobjective optimizers: an analysis and review publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2003.810758 – year: 2011 ident: 10.1016/j.ejor.2013.10.043_b0155 article-title: Sequential model-based optimization for general algorithm configuration – volume: 160 start-page: 889 issue: 8 year: 2000 ident: 10.1016/j.ejor.2013.10.043_b0265 article-title: MAX-MIN ant system publication-title: Future Generation Computer Systems doi: 10.1016/S0167-739X(00)00043-1 – volume: 70 start-page: 77 issue: 1 year: 2001 ident: 10.1016/j.ejor.2013.10.043_b0085 article-title: Using experimental design to find effective parameter settings for heuristics publication-title: Journal of Heuristics doi: 10.1023/A:1026569813391 – volume: 425 start-page: 0 75 year: 2012 ident: 10.1016/j.ejor.2013.10.043_b0030 article-title: Hypervolume-based multiobjective optimization: Theoretical foundations and practical implications publication-title: Theoretical Computer Science doi: 10.1016/j.tcs.2011.03.012 – start-page: 11 year: 2002 ident: 10.1016/j.ejor.2013.10.043_b0060 article-title: A racing algorithm for configuring metaheuristics 2002 – volume: 10 start-page: 0 53 issue: 1 year: 1997 ident: 10.1016/j.ejor.2013.10.043_b0100 article-title: Ant colony system: A cooperative learning approach to the traveling salesman problem publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/4235.585892 – volume: 170 start-page: 0 73 issue: 3 year: 1996 ident: 10.1016/j.ejor.2013.10.043_b0290 article-title: Using anytime algorithms in intelligent systems publication-title: AI Magazine – start-page: 209 year: 2010 ident: 10.1016/j.ejor.2013.10.043_b0210 article-title: Exploratory analysis of stochastic local search algorithms in biobjective optimization – start-page: 311 year: 2010 ident: 10.1016/j.ejor.2013.10.043_b0065 article-title: F-race and iterated F-race: An overview – volume: Vol. 4771 start-page: 108 year: 2007 ident: 10.1016/j.ejor.2013.10.043_b0040 article-title: Improvement strategies for the F-race algorithm: Sampling design and iterative refinement – volume: 130 start-page: 1120 issue: 5 year: 2009 ident: 10.1016/j.ejor.2013.10.043_b0240 article-title: Frankenstein’s PSO: A composite particle swarm optimization algorithm publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2009.2021465 – start-page: 191 year: 2012 ident: 10.1016/j.ejor.2013.10.043_b0270 article-title: Parameter adaptation in ant colony optimization – volume: 10 start-page: 0 222 issue: 3 year: 2011 ident: 10.1016/j.ejor.2013.10.043_b0285 article-title: Research note: the point of diminishing returns in heuristic search publication-title: International Journal of Metaheuristics doi: 10.1504/IJMHEUR.2011.041195 – volume: Vol. 6238 start-page: 728 year: 2010 ident: 10.1016/j.ejor.2013.10.043_b0280 article-title: Parameter tuning boosts performance of variation operators in multiobjective optimization – volume: 110 start-page: 193 issue: 1–5 year: 1997 ident: 10.1016/j.ejor.2013.10.043_b0230 article-title: The racing algorithm: Model selection for lazy learners publication-title: Artificial Intelligence Research doi: 10.1023/A:1006556606079 – start-page: 183 year: 2006 ident: 10.1016/j.ejor.2013.10.043_b0245 article-title: A method for parameter calibration and relevance estimation in evolutionary algorithms – volume: 10 start-page: 19 issue: 1 year: 2011 ident: 10.1016/j.ejor.2013.10.043_b0125 article-title: Parameter tuning for configuring and analyzing evolutionary algorithms publication-title: Swarm and Evolutionary Computation doi: 10.1016/j.swevo.2011.02.001 – volume: 90 start-page: 0 527 issue: 2 year: 2009 ident: 10.1016/j.ejor.2013.10.043_b0015 article-title: Adaptive parameter control of evolutionary algorithms to improve quality-time trade-off publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2008.07.001 – start-page: 263 year: 2011 ident: 10.1016/j.ejor.2013.10.043_b0070 article-title: Simultaneous tuning of metaheuristic parameters for various computing budgets – volume: Vol. 7491 start-page: 357 year: 2012 ident: 10.1016/j.ejor.2013.10.043_b0205 article-title: On the anytime behavior of IPOP-CMA-ES – start-page: 3823 year: 2010 ident: 10.1016/j.ejor.2013.10.043_b0235 article-title: Pre-scheduled and adaptive parameter variation in MAX-MIN Ant System – year: 2004 ident: 10.1016/j.ejor.2013.10.043_b0105 – volume: Vol. 6234 start-page: 95 year: 2010 ident: 10.1016/j.ejor.2013.10.043_b0215 article-title: Automatic configuration of multi-objective ACO algorithms – ident: 10.1016/j.ejor.2013.10.043_b0260 – start-page: 19 year: 2007 ident: 10.1016/j.ejor.2013.10.043_b0120 article-title: Parameter control in evolutionary algorithms – volume: Vol. 5732 start-page: 142 year: 2009 ident: 10.1016/j.ejor.2013.10.043_b0020 article-title: A gender-based genetic algorithm for the automatic configuration of algorithms – start-page: 2019 year: 2011 ident: 10.1016/j.ejor.2013.10.043_b0115 article-title: Automatic configuration of state-of-the-art multi-objective optimizers using the TP+PLS framework – ident: 10.1016/j.ejor.2013.10.043_b0165
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Snippet	•A method to automatically improve the anytime behaviour of optimisation algorithms.•Anytime behaviour is evaluated by the hypervolume... Optimisation algorithms with good anytime behaviour try to return as high-quality solutions as possible independently of the computation time allowed....
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SubjectTerms	Algorithms Anytime algorithms Automatic configuration Automation Computation Integer programming Mathematical models Metaheuristics Offline tuning Optimization Optimization algorithms Source code Studies Tradeoff analysis Tradeoffs Tuning
Title	Automatically improving the anytime behaviour of optimisation algorithms
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