Unbalanced budget distribution for automatic algorithm configuration
Optimization algorithms often have several critical setting parameters and the improvement of the empirical performance of these algorithms depends on tuning them. Manually configuration of such parameters is a tedious task that results in unsatisfactory outputs. Therefore, several automatic algorit...
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| Vydáno v: | Soft computing (Berlin, Germany) Ročník 26; číslo 3; s. 1315 - 1330 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Berlin/Heidelberg
Springer Berlin Heidelberg
01.02.2022
Springer Verlag |
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| ISSN: | 1432-7643, 1433-7479 |
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| Abstract | Optimization algorithms often have several critical setting parameters and the improvement of the empirical performance of these algorithms depends on tuning them. Manually configuration of such parameters is a tedious task that results in unsatisfactory outputs. Therefore, several automatic algorithm configuration frameworks have been proposed to regulate the parameters of a given algorithm for a series of problem instances. Although the developed frameworks perform very well to deal with various problems, however, there is still a trade-off between the accuracy and budget requirements that need to be addressed. This work investigates the performance of unbalanced distribution of budget for different configurations to deal with the automatic algorithm configuration problem. Inspired by the bandit-based approaches, the main goal is to find a better configuration that substantially improves the performance of the target algorithm while using a smaller run time budget. In this work, non-dominated sorting genetic algorithm II is employed as a target algorithm using jMetalPy software platform and the multimodal multi-objective optimization (MMO) test suite of CEC’2020 is used as a set of test problems. We did a comprehensive comparison with other known methods including random search, Bayesian optimization, sequential model-based algorithm configuration (SMAC), iterated local search in parameter configuration space (ParamILS), iterated racing for automatic algorithm configuration (irace), and many-objective automatic algorithm configuration (MAC) methods. In order to characterize, validate and evaluate the performance of these methods, hypervolume (HV), generational distance, and epsilon indicator (
I
ϵ
+
) are used as performance indicators. The experimental results interestingly proved the efficiency of the proposed approach for automatic algorithm configuration with a minimum time budget in comparison with other competitors. |
|---|---|
| AbstractList | Optimization algorithms often have several critical setting parameters and the improvement of the empirical performance of these algorithms depends on tuning them. Manually configuration of such parameters is a tedious task that results in unsatisfactory outputs. Therefore, several automatic algorithm configuration frameworks have been proposed to regulate the parameters of a given algorithm for a series of problem instances. Although the developed frameworks perform very well to deal with various problems, however, there is still a trade-off between the accuracy and budget requirements that need to be addressed. This work investigates the performance of unbalanced distribution of budget for different configurations to deal with the automatic algorithm configuration problem. Inspired by the bandit-based approaches, the main goal is to find a better configuration that substantially improves the performance of the target algorithm while using a smaller run time budget. In this work, non-dominated sorting genetic algorithm II is employed as a target algorithm using jMetalPy software platform and the multimodal multi-objective optimization (MMO) test suite of CEC’2020 is used as a set of test problems. We did a comprehensive comparison with other known methods including random search, Bayesian optimization, sequential model-based algorithm configuration (SMAC), iterated local search in parameter configuration space (ParamILS), iterated racing for automatic algorithm configuration (irace), and many-objective automatic algorithm configuration (MAC) methods. In order to characterize, validate and evaluate the performance of these methods, hypervolume (HV), generational distance, and epsilon indicator (
I
ϵ
+
) are used as performance indicators. The experimental results interestingly proved the efficiency of the proposed approach for automatic algorithm configuration with a minimum time budget in comparison with other competitors. |
| Author | Idoumghar, Lhassane Lepagnot, Julien Jourdan, Laetitia Rakhshani, Hojjat Ghambari, Soheila |
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| Cites_doi | 10.1023/A:1006556606079 10.1016/j.ins.2020.11.040 10.1145/3377930.3389831 10.1007/s11227-021-03915-0 10.1007/978-3-642-02538-9_13 10.1007/s10586-021-03291-7 10.3233/HIS-210008 10.1007/3-540-45356-3_83 10.1007/978-3-642-25566-3_40 10.1109/JPROC.2015.2494218 10.1016/j.neucom.2020.07.061 10.1109/CEC48606.2020.9185695 10.1007/s10462-020-09906-6 10.1007/978-3-030-18764-4 10.1109/4235.771166 10.1007/978-3-319-54157-0_5 10.1016/j.cie.2021.107250 10.1016/j.swevo.2021.100888 10.1016/j.swevo.2019.100598 10.1109/ICTAI50040.2020.00027 10.1016/j.cma.2020.113609 10.1007/s10479-021-04075-3 10.1007/978-3-030-12598-1_20 10.1613/jair.2861 10.1109/4235.797969 |
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| Issue | 3 |
| Keywords | Deep learning Automatic algorithm configuration Multi-objective optimization Bandit-based approaches |
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| References | Bartz-Beielstein T, Filipič B, Korošec P, Talbi EG (2020) High-performance simulation-based optimization. Springer ZitzlerEThieleLMultiobjective evolutionary algorithms: a comparative case study and the strength pareto approachIEEE Trans Evol Comput19993425727110.1109/4235.797969 EibenÁEHinterdingRMichalewiczZParameter control in evolutionary algorithmsIEEE Trans Evol Comput19993212414110.1109/4235.771166 Rakhshani H, Idoumghar L, Lepagnot J, Brévilliers M (2019) Mac: many-objective automatic algorithm configuration. In: International conference on evolutionary multi-criterion optimization. Springer, pp 241–253 Abualigah L, Alkhrabsheh M (2021) Amended hybrid multi-verse optimizer with genetic algorithm for solving task scheduling problem in cloud computing. J Supercomput:1–26 Hutter F, Hoos HH, Leyton-Brown K (2011) Sequential model-based optimization for general algorithm configuration. In: International conference on learning and intelligent optimization. Springer, pp 507–523 LiLJamiesonKDeSalvoGRostamizadehATalwalkarAHyperband: a novel bandit-based approach to hyperparameter optimizationJ Mach Learn Res20171816765681638270731468.68204 Vermetten D, Wang H, Doerr C, Back T (2020) Integrated vs. sequential approaches for selecting and tuning cma-es variants. In: ACM genetic and evolutionary computation conference (GECCO’20) MaronOMooreAWThe racing algorithm: model selection for lazy learnersArtif Intell Rev1997111–519322510.1023/A:1006556606079 Deb K, Agrawal S, Pratap A, Meyarivan T (2000) A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: Nsga-ii. In: International conference on parallel problem solving from nature. Springer, pp 849–858 HalimAHIsmailIDasSPerformance assessment of the metaheuristic optimization algorithms: an exhaustive reviewArtif Intell Rev20215432323240910.1007/s10462-020-09906-6 Hutter F, Hoos HH, Stützle T (2007) Automatic algorithm configuration based on local search. In: Aaai, vol 7, pp 1152–1157 FriedmanJHastieTTibshiraniRThe elements of statistical learning2001New YorkSpringer0973.62007 HutterFHoosHHLeyton-BrownKStützleTParamils: an automatic algorithm configuration frameworkJ Artif Intell Res20093626730610.1613/jair.2861 Benitez-HidalgoANebroAJGarcia-NietoJOregiIDel SerJjmetalpy: a python framework for multi-objective optimization with metaheuristicsSwarm Evol Comput20195110059810.1016/j.swevo.2019.100598 Golabi M, Ghambari S, Lepagnot J, Jourdan L, Brévilliers M, Idoumghar L (2020) Bypassing or flying above the obstacles? A novel multi-objective uav path planning problem. In: 2020 IEEE congress on evolutionary computation (CEC). IEEE, pp 1–8 Jamieson K, Talwalkar A (2016) Non-stochastic best arm identification and hyperparameter optimization. In: Artificial intelligence and statistics, pp 240–248 PalakondaVMallipeddiRSuganthanPNAn ensemble approach with external archive for multi-and many-objective optimization with adaptive mating mechanism and two-level environmental selectionInf Sci2021555164197419819210.1016/j.ins.2020.11.040 Falkner S, Klein A, Hutter F (2018) Bohb: robust and efficient hyperparameter optimization at scale. In: International conference on machine learning. PMLR, pp 1437–1446 Birattari M, Yuan Z, Balaprakash P, Stützle T (2010) F-race and iterated f-race: an overview. In: Experimental methods for the analysis of optimization algorithms. Springer, pp 311–336 Stützle T (2016) Automatic algorithm configuration: methods, applications, and perspectives. In: IJCCI (ECTA), p 7 ShahriariBSwerskyKWangZAdamsRPDe FreitasNTaking the human out of the loop: a review of bayesian optimizationProc IEEE2015104114817510.1109/JPROC.2015.2494218 López-IbáñezMDubois-LacosteJCáceresLPBirattariMStützleTThe irace package: iterated racing for automatic algorithm configurationOper Res Persp2016343583579175 Ghambari S, Golabi M, Lepagnot J, Brévilliers M, Jourdan L, Idoumghar L (2020) An enhanced nsga-ii for multiobjective uav path planning in urban environments. In: 2020 IEEE 32nd international conference on tools with artificial intelligence (ICTAI), pp 106–111 . https://doi.org/10.1109/ICTAI50040.2020.00027 YangLShamiAOn hyperparameter optimization of machine learning algorithms: theory and practiceNeurocomputing202041529531610.1016/j.neucom.2020.07.061 Blot A, Pernet A, Jourdan L, Kessaci-Marmion M.É, Hoos HH (2017) Automatically configuring multi-objective local search using multi-objective optimisation. In: International conference on evolutionary multi-criterion optimization. Springer, pp 61–76 Abualigah L, Diabat A, Mirjalili S, Abd Elaziz M, Gandomi AH (2021b) The arithmetic optimization algorithm. Comput Methods Appl Mech Eng 376:113609 Abualigah L, Yousri D, Abd Elaziz M, Ewees AA, Al-qaness MA, Gandomi AH (2021c) Aquila optimizer: a novel meta-heuristic optimization algorithm. Comput Ind Eng 157:107250 Bharati S, Podder P, Mondal M, Prasath V (2021) Co-resnet: optimized resnet model for covid-19 diagnosis from x-ray images. Int J Hybrid Intell Syst (Preprint):1–15 Hutter F (2009) Automated configuration of algorithms for solving hard computational problems. Ph.D. thesis, University of British Columbia Corazza M, di Tollo G, Fasano G, Pesenti R (2021) A novel hybrid pso-based metaheuristic for costly portfolio selection problems. Ann Oper Res:1–29 Abualigah L, Diabat A, Abd Elaziz M (2021a) Intelligent workflow scheduling for big data applications in iot cloud computing environments. Cluster Comput:1–20 Osaba E, Villar-Rodriguez E, Del Ser J, Nebro AJ, Molina D, LaTorre A, Suganthan PN, Coello CAC, Herrera F (2021) A tutorial on the design, experimentation and application of metaheuristic algorithms to real-world optimization problems. Swarm Evol Comput:100888 BergstraJBengioYRandom search for hyper-parameter optimizationJ Mach Learn Res201213128130529137011283.68282 Eggensperger K, Feurer M, Hutter F, Bergstra J, Snoek J, Hoos H, Leyton-Brown K (2013) Towards an empirical foundation for assessing bayesian optimization of hyperparameters. In: NIPS workshop on Bayesian optimization in theory and practice, vol 10, p 3 Liang J, Yue C, Li G, Qu B, Suganthan P, Yu K (2020) Problem definitions and evaluation criteria for the cec 2021 on multimodal multiobjective path planning optimization. Zhengzhou University and Nanyang Technological University, Tech. rep 6403_CR4 6403_CR3 6403_CR2 6403_CR1 J Friedman (6403_CR16) 2001 6403_CR26 B Shahriari (6403_CR32) 2015; 104 L Yang (6403_CR35) 2020; 415 6403_CR9 6403_CR8 6403_CR29 AH Halim (6403_CR19) 2021; 54 6403_CR5 L Li (6403_CR25) 2017; 18 6403_CR20 6403_CR21 6403_CR24 6403_CR23 ÁE Eiben (6403_CR14) 1999; 3 M López-Ibáñez (6403_CR27) 2016; 3 J Bergstra (6403_CR7) 2012; 13 V Palakonda (6403_CR30) 2021; 555 6403_CR15 F Hutter (6403_CR22) 2009; 36 6403_CR17 A Benitez-Hidalgo (6403_CR6) 2019; 51 6403_CR18 6403_CR31 6403_CR11 6403_CR33 6403_CR10 6403_CR13 E Zitzler (6403_CR36) 1999; 3 6403_CR12 6403_CR34 O Maron (6403_CR28) 1997; 11 |
| References_xml | – reference: Birattari M, Yuan Z, Balaprakash P, Stützle T (2010) F-race and iterated f-race: an overview. In: Experimental methods for the analysis of optimization algorithms. Springer, pp 311–336 – reference: Liang J, Yue C, Li G, Qu B, Suganthan P, Yu K (2020) Problem definitions and evaluation criteria for the cec 2021 on multimodal multiobjective path planning optimization. Zhengzhou University and Nanyang Technological University, Tech. rep – reference: LiLJamiesonKDeSalvoGRostamizadehATalwalkarAHyperband: a novel bandit-based approach to hyperparameter optimizationJ Mach Learn Res20171816765681638270731468.68204 – reference: Benitez-HidalgoANebroAJGarcia-NietoJOregiIDel SerJjmetalpy: a python framework for multi-objective optimization with metaheuristicsSwarm Evol Comput20195110059810.1016/j.swevo.2019.100598 – reference: Osaba E, Villar-Rodriguez E, Del Ser J, Nebro AJ, Molina D, LaTorre A, Suganthan PN, Coello CAC, Herrera F (2021) A tutorial on the design, experimentation and application of metaheuristic algorithms to real-world optimization problems. Swarm Evol Comput:100888 – reference: Stützle T (2016) Automatic algorithm configuration: methods, applications, and perspectives. In: IJCCI (ECTA), p 7 – reference: Corazza M, di Tollo G, Fasano G, Pesenti R (2021) A novel hybrid pso-based metaheuristic for costly portfolio selection problems. Ann Oper Res:1–29 – reference: Deb K, Agrawal S, Pratap A, Meyarivan T (2000) A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: Nsga-ii. In: International conference on parallel problem solving from nature. Springer, pp 849–858 – reference: ShahriariBSwerskyKWangZAdamsRPDe FreitasNTaking the human out of the loop: a review of bayesian optimizationProc IEEE2015104114817510.1109/JPROC.2015.2494218 – reference: PalakondaVMallipeddiRSuganthanPNAn ensemble approach with external archive for multi-and many-objective optimization with adaptive mating mechanism and two-level environmental selectionInf Sci2021555164197419819210.1016/j.ins.2020.11.040 – reference: Abualigah L, Alkhrabsheh M (2021) Amended hybrid multi-verse optimizer with genetic algorithm for solving task scheduling problem in cloud computing. J Supercomput:1–26 – reference: Abualigah L, Diabat A, Mirjalili S, Abd Elaziz M, Gandomi AH (2021b) The arithmetic optimization algorithm. Comput Methods Appl Mech Eng 376:113609 – reference: Eggensperger K, Feurer M, Hutter F, Bergstra J, Snoek J, Hoos H, Leyton-Brown K (2013) Towards an empirical foundation for assessing bayesian optimization of hyperparameters. In: NIPS workshop on Bayesian optimization in theory and practice, vol 10, p 3 – reference: Vermetten D, Wang H, Doerr C, Back T (2020) Integrated vs. sequential approaches for selecting and tuning cma-es variants. In: ACM genetic and evolutionary computation conference (GECCO’20) – reference: Hutter F, Hoos HH, Stützle T (2007) Automatic algorithm configuration based on local search. In: Aaai, vol 7, pp 1152–1157 – reference: Rakhshani H, Idoumghar L, Lepagnot J, Brévilliers M (2019) Mac: many-objective automatic algorithm configuration. In: International conference on evolutionary multi-criterion optimization. Springer, pp 241–253 – reference: Bartz-Beielstein T, Filipič B, Korošec P, Talbi EG (2020) High-performance simulation-based optimization. Springer – reference: López-IbáñezMDubois-LacosteJCáceresLPBirattariMStützleTThe irace package: iterated racing for automatic algorithm configurationOper Res Persp2016343583579175 – reference: YangLShamiAOn hyperparameter optimization of machine learning algorithms: theory and practiceNeurocomputing202041529531610.1016/j.neucom.2020.07.061 – reference: Hutter F, Hoos HH, Leyton-Brown K (2011) Sequential model-based optimization for general algorithm configuration. In: International conference on learning and intelligent optimization. Springer, pp 507–523 – reference: Bharati S, Podder P, Mondal M, Prasath V (2021) Co-resnet: optimized resnet model for covid-19 diagnosis from x-ray images. Int J Hybrid Intell Syst (Preprint):1–15 – reference: Golabi M, Ghambari S, Lepagnot J, Jourdan L, Brévilliers M, Idoumghar L (2020) Bypassing or flying above the obstacles? A novel multi-objective uav path planning problem. In: 2020 IEEE congress on evolutionary computation (CEC). IEEE, pp 1–8 – reference: ZitzlerEThieleLMultiobjective evolutionary algorithms: a comparative case study and the strength pareto approachIEEE Trans Evol Comput19993425727110.1109/4235.797969 – reference: Abualigah L, Yousri D, Abd Elaziz M, Ewees AA, Al-qaness MA, Gandomi AH (2021c) Aquila optimizer: a novel meta-heuristic optimization algorithm. Comput Ind Eng 157:107250 – reference: Jamieson K, Talwalkar A (2016) Non-stochastic best arm identification and hyperparameter optimization. In: Artificial intelligence and statistics, pp 240–248 – reference: FriedmanJHastieTTibshiraniRThe elements of statistical learning2001New YorkSpringer0973.62007 – reference: EibenÁEHinterdingRMichalewiczZParameter control in evolutionary algorithmsIEEE Trans Evol Comput19993212414110.1109/4235.771166 – reference: HutterFHoosHHLeyton-BrownKStützleTParamils: an automatic algorithm configuration frameworkJ Artif Intell Res20093626730610.1613/jair.2861 – reference: MaronOMooreAWThe racing algorithm: model selection for lazy learnersArtif Intell Rev1997111–519322510.1023/A:1006556606079 – reference: BergstraJBengioYRandom search for hyper-parameter optimizationJ Mach Learn Res201213128130529137011283.68282 – reference: Falkner S, Klein A, Hutter F (2018) Bohb: robust and efficient hyperparameter optimization at scale. In: International conference on machine learning. PMLR, pp 1437–1446 – reference: Abualigah L, Diabat A, Abd Elaziz M (2021a) Intelligent workflow scheduling for big data applications in iot cloud computing environments. Cluster Comput:1–20 – reference: HalimAHIsmailIDasSPerformance assessment of the metaheuristic optimization algorithms: an exhaustive reviewArtif Intell Rev20215432323240910.1007/s10462-020-09906-6 – reference: Hutter F (2009) Automated configuration of algorithms for solving hard computational problems. Ph.D. thesis, University of British Columbia – reference: Blot A, Pernet A, Jourdan L, Kessaci-Marmion M.É, Hoos HH (2017) Automatically configuring multi-objective local search using multi-objective optimisation. In: International conference on evolutionary multi-criterion optimization. Springer, pp 61–76 – reference: Ghambari S, Golabi M, Lepagnot J, Brévilliers M, Jourdan L, Idoumghar L (2020) An enhanced nsga-ii for multiobjective uav path planning in urban environments. 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| Title | Unbalanced budget distribution for automatic algorithm configuration |
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