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Andersson, Martin
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Andersson, M. (2018). A bilevel approach to parameter tuning of optimization algorithms using evolutionary computing: Understanding optimization algorithms through optimization. (Doctoral dissertation). Skövde: University of Skövde
Öppna denna publikation i ny flik eller fönster >>A bilevel approach to parameter tuning of optimization algorithms using evolutionary computing: Understanding optimization algorithms through optimization
2018 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

Most optimization problems found in the real world cannot be solved using analytical methods. For these types of difficult optimization problems, an alternative approach is needed. Metaheuristics are a category of optimization algorithms that do not guarantee that an optimal solution will be found, but instead search for the best solutions using some general heuristics. Metaheuristics have been shown to be effective at finding “good-enough” solutions to a wide variety of difficult problems. Most metaheuristics involve control parameters that can be used to modify how the heuristics perform its search. This is necessary because different problems may require different search strategies to be solved effectively. The control parameters allow for the optimization algorithm to be adapted to the problem at hand. It is, however, difficult to predict what the optimal control parameters are for any given problem. The problem of finding these optimal control parameter values is known as parameter tuning and is the main topic of this thesis. This thesis uses a bilevel optimization approach to solve parameter tuning problems. In this approach, the parameter tuning problem itself is formulated as an optimization problem and solved with an optimization algorithm. The parameter tuning problem formulated as a bilevel optimization problem is challenging because of nonlinear objective functions, interacting variables, multiple local optima, and noise. However, it is in precisely this kind of difficult optimization problem that evolutionary algorithms, which are a subclass of metaheuristics, have been shown to be effective. That is the motivation for using evolutionary algorithms for the upper-level optimization (i.e. tuning algorithm) of the bilevel optimization approach. Solving the parameter tuning problem using a bilevel optimization approach is also computationally expensive, since a complete optimization run has to be completed for every evaluation of a set of control parameter values. It is therefore important that the tuning algorithm be as efficient as possible, so that the parameter tuning problem can be solved to a satisfactory level with relatively few evaluations. Even so, bilevel optimization experiments can take a long time to run on a single computer. There is, however, considerable parallelization potential in the bilevel optimization approach, since many of the optimizations are independent of one another. This thesis has three primary aims: first, to present a bilevel optimization framework and software architecture for parallel parameter tuning; second, to use this framework and software architecture to evaluate and configure evolutionary algorithms as tuners and compare them with other parameter tuning methods; and, finally, to use parameter tuning experiments to gain new insights into and understanding of how optimization algorithms work and how they can used be to their maximum potential. The proposed framework and software architecture have been implemented and deployed in more than one hundred computers running many thousands of parameter tuning experiments for many millions of optimizations. This illustrates that this design and implementation approach can handle large parameter tuning experiments. Two types of evolutionary algorithms, i.e. differential evolution (DE) and a genetic algorithm (GA), have been evaluated as tuners against the parameter tuning algorithm irace. The as pects of algorithm configuration and noise handling for DE and the GA as related to the parameter tuning problem were also investigated. The results indicate that dynamic resampling strategies outperform static resampling strategies. It was also shown that the GA needs an explicit exploration and exploitation strategy in order not become stuck in local optima. The comparison with irace shows that both DE and the GA can significantly outperform it in a variety of different tuning problems.

Ort, förlag, år, upplaga, sidor
Skövde: University of Skövde, 2018. s. 210
Serie
Dissertation Series ; 25
Nationell ämneskategori
Systemvetenskap, informationssystem och informatik med samhällsvetenskaplig inriktning
Forskningsämne
Produktion och automatiseringsteknik
Identifikatorer
urn:nbn:se:his:diva-16368 (URN)978-91-984187-7-4 (ISBN)
Disputation
2018-09-24, ASSAR Industrial Innovation Arena, Skövde, 10:00
Opponent
Handledare
Tillgänglig från: 2018-11-15 Skapad: 2018-11-07 Senast uppdaterad: 2018-11-15Bibliografiskt granskad
Andersson, M. & Ng, A. H. C. (2018). Parameter Tuning Evolutionary Algorithms for Runtime versus Cost Trade-off in a Cloud Computing Environment. Simulation Modelling Practice and Theory, 89, 195-205
Öppna denna publikation i ny flik eller fönster >>Parameter Tuning Evolutionary Algorithms for Runtime versus Cost Trade-off in a Cloud Computing Environment
2018 (Engelska)Ingår i: Simulation Modelling Practice and Theory, ISSN 1569-190X, Vol. 89, s. 195-205Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

The runtime of an evolutionary algorithm can be reduced by increasing the number of parallel evaluations. However, increasing the number of parallel evaluations can also result in wasted computational effort since there is a greater probability of creating solutions that do not contribute to convergence towards the global optimum. A trade-off, therefore, arises between the runtime and computational effort for different levels of parallelization of an evolutionary algorithm.  When the computational effort is translated into cost, the trade-off can be restated as runtime versus cost. This trade-off is particularly relevant for cloud computing environments where the computing resources can be exactly matched to the level of parallelization of the algorithm, and the cost is proportional to the runtime and how many instances that are used. This paper empirically investigates this trade-off for two different evolutionary algorithms, NSGA-II and differential evolution (DE) when applied to multi-objective discrete-event simulation-based (DES) problem. Both generational and steadystate asynchronous versions of both algorithms are included. The approach is to perform parameter tuning on a simplified version of the DES model. A subset of the best configurations from each tuning experiment is then evaluated on a cloud computing platform. The results indicate that, for the included DES problem, the steady-state asynchronous version of each algorithm provides a better runtime versus cost trade-off than the generational versions and that DE outperforms NSGA-II.

Ort, förlag, år, upplaga, sidor
Elsevier, 2018
Nationell ämneskategori
Datavetenskap (datalogi)
Forskningsämne
Produktion och automatiseringsteknik
Identifikatorer
urn:nbn:se:his:diva-16273 (URN)10.1016/j.simpat.2018.10.003 (DOI)000450450400013 ()2-s2.0-85055089735 (Scopus ID)
Tillgänglig från: 2018-10-04 Skapad: 2018-10-04 Senast uppdaterad: 2019-02-05Bibliografiskt granskad
Bandaru, S., Andersson, M. & Ng, A. H. C. (2016). Metamodel-based prediction of performance metrics for bilevel parameter tuning in MOEAs. In: 2016 IEEE Congress on Evolutionary Computation (CEC): . Paper presented at 2016 IEEE Congress on Evolutionary Computation, CEC 2016, Vancouver, Canada, July 24-29, 2016 (pp. 1909-1916). New York: IEEE
Öppna denna publikation i ny flik eller fönster >>Metamodel-based prediction of performance metrics for bilevel parameter tuning in MOEAs
2016 (Engelska)Ingår i: 2016 IEEE Congress on Evolutionary Computation (CEC), New York: IEEE, 2016, s. 1909-1916Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

We consider a bilevel parameter tuning problem where the goal is to maximize the performance of a given multi-objective evolutionary optimizer on a given problem. The search for optimal algorithmic parameters requires the assessment of several sets of parameters, through multiple optimization runs, in order to mitigate the effect of noise that is inherent to evolutionary algorithms. This task is computationally expensive and therefore, in this paper, we propose to use sampling and metamodeling to approximate the performance of the optimizer as a function of its parameters. While such an approach is not unheard of, the choice of the metamodel to be used still remains unclear. The aim of this paper is to empirically compare 11 different metamodeling techniques with respect to their accuracy and training times in predicting two popular multi-objective performance metrics, namely, the hypervolume and the inverted generational distance. For the experiments in this pilot study, NSGA-II is used as the multi-objective optimizer for solving ZDT problems, 1 through 4.

Ort, förlag, år, upplaga, sidor
New York: IEEE, 2016
Nyckelord
Parameter tuning, Evolutionary computation, Metamodeling, Bilevel optimization
Nationell ämneskategori
Datavetenskap (datalogi)
Forskningsämne
Teknik; Produktion och automatiseringsteknik
Identifikatorer
urn:nbn:se:his:diva-13285 (URN)10.1109/CEC.2016.7744021 (DOI)000390749102012 ()2-s2.0-85008256466 (Scopus ID)978-1-5090-0623-6 (ISBN)978-1-5090-0622-9 (ISBN)978-1-5090-0624-3 (ISBN)
Konferens
2016 IEEE Congress on Evolutionary Computation, CEC 2016, Vancouver, Canada, July 24-29, 2016
Tillgänglig från: 2017-01-02 Skapad: 2017-01-02 Senast uppdaterad: 2018-03-28Bibliografiskt granskad
Andersson, M., Bandaru, S. & Ng, A. H. C. (2016). Towards Optimal Algorithmic Parameters for Simulation-Based Multi-Objective Optimization. In: 2016 IEEE Congress on Evolutionary Computation (CEC): . Paper presented at 2016 IEEE Congress on Evolutionary Computation, CEC 2016, Vancouver, Canada, July 24-29, 2016 (pp. 5162-5169). New York: IEEE
Öppna denna publikation i ny flik eller fönster >>Towards Optimal Algorithmic Parameters for Simulation-Based Multi-Objective Optimization
2016 (Engelska)Ingår i: 2016 IEEE Congress on Evolutionary Computation (CEC), New York: IEEE, 2016, s. 5162-5169Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

The use of optimization to solve a simulation-based multi-objective problem produces a set of solutions that provide information about the trade-offs that have to be considered by the decision maker. An incomplete or sub-optimal set of solutions will negatively affect the quality of any subsequent decisions. The parameters that control the search behavior of an optimization algorithm can be used to minimize this risk. However, choosing good parameter settings for a given optimization algorithm and problem combination is difficult. The aim of this paper is to take a step towards optimal parameter settings for optimization of simulation-based problems. Two parameter tuning methods, Latin Hypercube Sampling and Genetic Algorithms, are used to maximize the performance of NSGA-II applied to a simulation-based problem with discrete variables. The strengths and weaknesses of both methods are analyzed. The effect of the number of decision variables and the function budget on the optimal parameter settings is also studied.

Ort, förlag, år, upplaga, sidor
New York: IEEE, 2016
Serie
IEEE Congress on Evolutionary Computation
Nationell ämneskategori
Datavetenskap (datalogi)
Forskningsämne
Produktion och automatiseringsteknik
Identifikatorer
urn:nbn:se:his:diva-13331 (URN)10.1109/CEC.2016.7748344 (DOI)000390749105045 ()2-s2.0-85008258262 (Scopus ID)978-1-5090-0623-6 (ISBN)978-1-5090-0622-9 (ISBN)978-1-5090-0624-3 (ISBN)
Konferens
2016 IEEE Congress on Evolutionary Computation, CEC 2016, Vancouver, Canada, July 24-29, 2016
Tillgänglig från: 2017-01-20 Skapad: 2017-01-20 Senast uppdaterad: 2018-11-07Bibliografiskt granskad
Andersson, M., Bandaru, S. & Ng, A. H. C. (2016). Tuning of Multiple Parameter Sets in Evolutionary Algorithms. In: GECCO'16: Proceedings of the 2016 genetic and evolutionary computation conference. Paper presented at Genetic and Evolutionary Computation Conference (GECCO), Denver, USA, July 20-24, 2016. (pp. 533-540). Association for Computing Machinery (ACM)
Öppna denna publikation i ny flik eller fönster >>Tuning of Multiple Parameter Sets in Evolutionary Algorithms
2016 (Engelska)Ingår i: GECCO'16: Proceedings of the 2016 genetic and evolutionary computation conference, Association for Computing Machinery (ACM), 2016, s. 533-540Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Evolutionary optimization algorithms typically use one or more parameters that control their behavior. These parameters, which are often kept constant, can be tuned to improve the performance of the algorithm on specific problems. However, past studies have indicated that the performance can be further improved by adapting the parameters during runtime. A limitation of these studies is that they only control, at most, a few parameters, thereby missing potentially beneficial interactions between them. Instead of finding a direct control mechanism, the novel approach in this paper is to use different parameter sets in different stages of an optimization. These multiple parameter sets, which remain static within each stage, are tuned through extensive bi-level optimization experiments that approximate the optimal adaptation of the parameters. The algorithmic performance obtained with tuned multiple parameter sets is compared against that obtained with a single parameter set. For the experiments in this paper, the parameters of NSGA-II are tuned when applied to the ZDT, DTLZ and WFG test problems. The results show that using multiple parameter sets can significantly increase the performance over a single parameter set.

Ort, förlag, år, upplaga, sidor
Association for Computing Machinery (ACM), 2016
Nyckelord
evolutionary algorithms, parameter tuning, multiple parameters, multi-objective optimization
Nationell ämneskategori
Datavetenskap (datalogi)
Forskningsämne
Produktion och automatiseringsteknik
Identifikatorer
urn:nbn:se:his:diva-13056 (URN)10.1145/2908812.2908899 (DOI)000382659200069 ()2-s2.0-84985916855 (Scopus ID)978-1-4503-4206-3 (ISBN)
Konferens
Genetic and Evolutionary Computation Conference (GECCO), Denver, USA, July 20-24, 2016.
Tillgänglig från: 2016-10-27 Skapad: 2016-10-27 Senast uppdaterad: 2018-11-07Bibliografiskt granskad
Syberfeldt, A., Andersson, M., Ng, A. & Bengtsson, V. (2015). Multi-Objective Evolutionary Optimization of Personnel Scheduling. International Journal of Artificial Intelligence & Applications, 6(1), 41-52
Öppna denna publikation i ny flik eller fönster >>Multi-Objective Evolutionary Optimization of Personnel Scheduling
2015 (Engelska)Ingår i: International Journal of Artificial Intelligence & Applications, ISSN 0976-2191, E-ISSN 0975-900X, Vol. 6, nr 1, s. 41-52Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

This paper presents an evolutionary multi-objective simulation-optimization system for personnelscheduling. The system is developed for the Swedish postal services and aims at finding personnelschedules that minimizes both total man hours and the administrative burden of the person responsible forhandling schedules. For the optimization, the multi-objective evolutionary algorithm NSGA-II isimplemented. In order to make the optimization fast enough, a two-level parallelisation model is beingadopted. The simulation-optimization system is evaluated on a real-world test case and results from theevaluation shows that the algorithm is successful in optimizing the problem.

Ort, förlag, år, upplaga, sidor
AIRCC Publishing Corporation, 2015
Nyckelord
Multi-objective evolutionary optimization, NSGA-II, hill climbing, personnel scheduling, case study.
Nationell ämneskategori
Produktionsteknik, arbetsvetenskap och ergonomi
Forskningsämne
Teknik; Produktion och automatiseringsteknik
Identifikatorer
urn:nbn:se:his:diva-10629 (URN)10.5121/ijaia.2015.6103 (DOI)
Forskningsfinansiär
KK-stiftelsen
Tillgänglig från: 2015-02-02 Skapad: 2015-02-02 Senast uppdaterad: 2018-03-29Bibliografiskt granskad
Andersson, M., Bandaru, S., Ng, A. H. C. & Syberfeldt, A. (2015). Parameter tuned CMA-ES on the CEC'15 expensive problems. In: Evolutionary Computation: . Paper presented at 2015 IEEE Congress on Evolutionary Computation (CEC) (pp. 1950-1957). IEEE conference proceedings
Öppna denna publikation i ny flik eller fönster >>Parameter tuned CMA-ES on the CEC'15 expensive problems
2015 (Engelska)Ingår i: Evolutionary Computation, IEEE conference proceedings, 2015, s. 1950-1957Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Evolutionary optimization algorithms have parameters that are used to adapt the search strategy to suit different optimization problems. Selecting the optimal parameter values for a given problem is difficult without a-priori knowledge. Experimental studies can provide this knowledge by finding the best parameter values for a specific set of problems. This knowledge can also be constructed into heuristics (rule-of-thumbs) that can adapt the parameters for the problem. The aim of this paper is to assess the heuristics of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) optimization algorithm. This is accomplished by tuning CMA-ES parameters so as to maximize its performance on the CEC'15 problems, using a bilevel optimization approach that searches for the optimal parameter values. The optimized parameter values are compared against the parameter values suggested by the heuristics. The difference between specialized and generalized parameter values are also investigated.

Ort, förlag, år, upplaga, sidor
IEEE conference proceedings, 2015
Nyckelord
Parameter tuning, CMA-ES
Nationell ämneskategori
Data- och informationsvetenskap
Forskningsämne
Teknik; Produktion och automatiseringsteknik
Identifikatorer
urn:nbn:se:his:diva-11599 (URN)10.1109/CEC.2015.7257124 (DOI)000380444801129 ()2-s2.0-84963626635 (Scopus ID)978-1-4799-7492-4 (ISBN)
Konferens
2015 IEEE Congress on Evolutionary Computation (CEC)
Tillgänglig från: 2015-10-12 Skapad: 2015-10-12 Senast uppdaterad: 2018-11-07Bibliografiskt granskad
Andersson, M., Bandaru, S., Ng, A. H. C. & Syberfeldt, A. (2015). Parameter Tuning of MOEAs Using a Bilevel Optimization Approach. In: António Gaspar-Cunha, Carlos Henggeler Antunes & Carlos Coello Coello (Ed.), Evolutionary Multi-Criterion Optimization: 8th International Conference, EMO 2015, Guimarães, Portugal, March 29 --April 1, 2015. Proceedings, Part I. Paper presented at 8th International Conference on Evolutionary Multi-Criterion Optimization, 29 March-1 April 2015, Guimarães, Portugal (pp. 233-247). Springer
Öppna denna publikation i ny flik eller fönster >>Parameter Tuning of MOEAs Using a Bilevel Optimization Approach
2015 (Engelska)Ingår i: Evolutionary Multi-Criterion Optimization: 8th International Conference, EMO 2015, Guimarães, Portugal, March 29 --April 1, 2015. Proceedings, Part I / [ed] António Gaspar-Cunha, Carlos Henggeler Antunes & Carlos Coello Coello, Springer, 2015, s. 233-247Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

The performance of an Evolutionary Algorithm (EA) can be greatly influenced by its parameters. The optimal parameter settings are also not necessarily the same across different problems. Finding the optimal set of parameters is therefore a difficult and often time-consuming task. This paper presents results of parameter tuning experiments on the NSGA-II and NSGA-III algorithms using the ZDT test problems. The aim is to gain new insights on the characteristics of the optimal parameter settings and to study if the parameters impose the same effect on both NSGA-II and NSGA-III. The experiments also aim at testing if the rule of thumb that the mutation probability should be set to one divided by the number of decision variables is a good heuristic on the ZDT problems. A comparison of the performance of NSGA-II and NSGA-III on the ZDT problems is also made.

Ort, förlag, år, upplaga, sidor
Springer, 2015
Serie
Lecture Notes in Computer Science, ISSN 0302-9743 ; 9018
Nyckelord
Parameter tuning, NSGA-II, NSGA-III, ZDT, Bilevel optimization, Multi-objective problems
Nationell ämneskategori
Data- och informationsvetenskap
Forskningsämne
Teknik; Produktion och automatiseringsteknik
Identifikatorer
urn:nbn:se:his:diva-11371 (URN)10.1007/978-3-319-15934-8_16 (DOI)000361702100016 ()2-s2.0-84925342559 (Scopus ID)978-3-319-15933-1 (ISBN)978-3-319-15934-8 (ISBN)
Konferens
8th International Conference on Evolutionary Multi-Criterion Optimization, 29 March-1 April 2015, Guimarães, Portugal
Tillgänglig från: 2015-08-18 Skapad: 2015-08-18 Senast uppdaterad: 2018-11-07Bibliografiskt granskad
Andersson, M., Syberfeldt, A., Ng, A. & Bengtsson, V. (2014). Evolutionary Simulation Optimization of Personnel Scheduling. In: Industrial Simulation Conference, Skövde, June 11-13, 2014: . Paper presented at ISC'2014, 12th Annual Industrial Simulation Conference, June 11-13, 2014, University of Skövde, Skövde, Sweden (pp. 61-65). Eurosis
Öppna denna publikation i ny flik eller fönster >>Evolutionary Simulation Optimization of Personnel Scheduling
2014 (Engelska)Ingår i: Industrial Simulation Conference, Skövde, June 11-13, 2014, Eurosis , 2014, s. 61-65Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

This paper presents a simulation-optimization system for personnel scheduling. The system is developed for the Swedish postal services and aims at finding personnel schedules that minimizes both total man hours and the administrative burden of the person responsible for handling schedules. For the optimization, the multi-objective evolutionary algorithm NSGA-II is implemented. The simulation-optimization system is evaluated on a real-world test case and results from the evaluation shows that the algorithm is successful in optimizing the problem.

Ort, förlag, år, upplaga, sidor
Eurosis, 2014
Nyckelord
Simulation, optimization, personnel scheduling, hill climbing, NSGA-II, case study
Nationell ämneskategori
Teknik och teknologier
Forskningsämne
Teknik; Produktion och automatiseringsteknik
Identifikatorer
urn:nbn:se:his:diva-9392 (URN)2-s2.0-84922156535 (Scopus ID)978-90-77381-83-0 (ISBN)
Konferens
ISC'2014, 12th Annual Industrial Simulation Conference, June 11-13, 2014, University of Skövde, Skövde, Sweden
Projekt
Blixtsim
Forskningsfinansiär
KK-stiftelsen
Tillgänglig från: 2014-06-09 Skapad: 2014-06-09 Senast uppdaterad: 2018-05-07Bibliografiskt granskad
Hossain, M., Harari, N., Semere, D., Mårtensson, P., Ng, A. H. H. & Andersson, M. (2012). Integrated Modeling and Application of Standardized Data Schema. In: Proceedings of the 5th Swedish Production Symposium (SPS 12): . Paper presented at The 5th International Swedish Production Symposium 6th – 8th of November 2012 Linköping, Sweden (pp. 473-478).
Öppna denna publikation i ny flik eller fönster >>Integrated Modeling and Application of Standardized Data Schema
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2012 (Engelska)Ingår i: Proceedings of the 5th Swedish Production Symposium (SPS 12), 2012, s. 473-478Konferensbidrag, Publicerat paper (Refereegranskat)
Nationell ämneskategori
Teknik och teknologier
Forskningsämne
Teknik
Identifikatorer
urn:nbn:se:his:diva-7318 (URN)978-91-7519-752-4 (ISBN)
Konferens
The 5th International Swedish Production Symposium 6th – 8th of November 2012 Linköping, Sweden
Tillgänglig från: 2013-02-26 Skapad: 2013-02-26 Senast uppdaterad: 2017-11-27Bibliografiskt granskad
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