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Conference on Drone Systems Engineering (DroneSE) / Conference on Rapid Simulation and Performance Evaluation - Methods and Tools (RAPIDO) / Workshop on System Engineering for Constrained Embedded Systems / HiPEAC Conference

作者： Steiner, Lukas Delazeri, Gustavo da Silva, Iron Prando Jung, Matthias Wehn, Norbert RPTU Kaiserslautern Landau Microelect Syst Design Res Grp Kaiserslautern Germany Fraunhofer Inst Expt Software Engn IESE Kaiserslautern Germany HTW Saar Saarbrucken Germany

ISBN: (纸本)9798400700453

Nowadays, DRAM subsystem configuration includes a large number of parameters, resulting in an extensive design space. Setting these parameters is a challenging step in system design as the parameter-workload interactions are complex. Since design space exploration by exhaustive simulation is infeasible due to limited computing resources and development time, semi-automatic configuration involving both manual as well as simulation-based decisions is state-of-the-art. However, it requires a lot of expertise in the DRAM domain as well as application knowledge, and there is no guarantee for a good performance of the resulting subsystem. In this paper, we present a new framework that fully automatizes the DRAM subsystem configuration for a given parameter space and set of target applications. It is based on irace, a software package originally developed for automatic configuration of optimization algorithms. We show that the framework finds nearly-optimal configurations, while only a fraction of all application-configuration combinations has to be evaluated. In addition, all returned configurations perform better than a predefined standard configuration. Thus, our framework enables designers to automatically determine a suitable DRAM subsystem for their platform.

关键词： DRAM Design Space Exploration irace automatic algorithm configuration

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Stochastic local search and parameters recommendation: a case study on flowshop problems

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INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2023年第2期30卷 774-799页

作者： Pavelski, Lucas M. Delgado, Myriam Kessaci, Marie-Eleonore Freitas, Alex A. Univ Tecnol Fed Parana CPGEI Av Sete Setembro Curitiba Parana Brazil Univ Lille Cent Lille CNRS UMR 9189CRIStAL F-59000 Lille France Univ Kent Sch Comp Canterbury Kent England

The algorithm Selection Problem (ASP) considers the use of previous knowledge regarding problem features and algorithm performance to recommend the best strategy to solve a previously unseen problem. In the application context, the usual ASP for optimization considers recommending the best heuristics, whenever it faces a new similar problem instance, also known as the Per-Instance ASP. Although ASP for heuristic recommendation is not new, selecting heuristics and also their parameters, or the Per-instance algorithm configuration Problem, is still considered a challenging task. This paper investigates the use of meta-learning to recommend six different stochastic local searches and their parameters to solve several instances of permutation flowshop problems. The proposed approach uses several problem features, including fitness landscape metrics, builds the performance database using irace, and trains different multi-label recommendation models on a data set with more than 6000 flowshop problem instances. Experiments show that decision tree-based machine learning models achieve good performance, and the quality of the recommendations is capable of outperforming the state-of-the-art algorithm with tuned configuration.

关键词： algorithm selection problem automatic algorithm configuration flowshop stochastic local search

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A landscape-based analysis of fixed temperature and simulated annealing

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2023年第2期304卷 395-410页

作者： Franzin, Alberto Stutzle, Thomas Univ Libre Bruxelles ULB LISA Brussels Belgium Univ Libre Bruxelles ULB IRIDIA Brussels Belgium

Since the introduction of Simulated Annealing (SA), researchers have considered variants that keep the same temperature value throughout the whole search and tried to determine whether this strategy can be more effective than the original cooling scheme. Several studied have tried to answer this question without a conclusive answer and without providing indications that could be useful for a practical imple-mentation. In this work, we address this question following an experimental approach, relating the char-acteristics of the algorithms with the characteristics of the landscapes they encounter. We use problem -independent landscape features to study the algorithmic behaviour across different problems. We con-sider three different objective functions and various instance classes and determine the conditions under which the fixed-temperature variant of SA can outperform its original counterpart and when SA is instead a better choice.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Metaheuristics Simulated annealing Stochastic local search automatic algorithm configuration Exploratory landscape analysis

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Efficient configuration of Optimization algorithms 22

Efficient Configuration of Optimization Algorithms

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Genetic and Evolutionary Computation Conference (GECCO)

作者： de Souza, Marcelo Ritt, Marcus Lopez-Ibanez, Manuel Univ Estado Santa Catarina Florianopolis SC Brazil Univ Fed Rio Grande do Sul Porto Alegre RS Brazil Univ Fed Rio Grande do Sul Inst Informat Porto Alegre RS Brazil Univ Malaga Malaga Spain Univ Manchester Manchester Lancs England

ISBN: (纸本)9781450392686

We propose a set of capping methods to speed-up the automatic configuration of optimization algorithms. First, we build a performance envelope based on previous executions of known configurations, which defines the minimum required performance for new configurations. Then, we use the performance envelope to evaluate new configurations, stopping poor performers early. We propose different methods to aggregate previous executions into a performance envelope, and evaluate them on several configuration scenarios. The proposed methods produce solutions of the same or better quality as configuring without capping, but reduce the effort required to configure optimization algorithms.

关键词： automatic algorithm configuration parameter tuning capping

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PyDGGA: Distributed GGA for automatic configuration 1

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24th International Conference on Theory and Applications of Satisfiability Testing (SAT)

作者： Ansotegui, Carlos Pon, Josep Sellmann, Meinolf Tierney, Kevin Univ Lleida LOG Grp Lleida Spain Gen Elect Boston MA 02210 USA Bielefeld Univ Decis & Operat Technol Grp Bielefeld Germany

ISBN: (数字)9783030802233

ISBN: (纸本)9783030802233;9783030802226

We present PYDGGA, a Python tool that implements a distributed version of the automatic algorithm configurator GGA, which is a specialized genetic algorithm to find high quality parameters for solvers and algorithms. PYDGGA implements GGA using an event-driven architecture and runs a simulation of future generations of the genetic algorithm to maximize the usage of the available computing resources. Overall, PYDGGA offers a friendly interface to deploy elastic distributed AC scenarios on shared high-performance computing clusters.

关键词： automatic algorithm configuration Satisfiability

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Component-wise Analysis of automatically Designed Multiobjective algorithms on Constrained Problems

Component-wise Analysis of Automatically Designed Multiobjec...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Lavinas, Yuri Ladeira, Marcelo Ochoa, Gabriela Aranha, Claus Univ Tsukuba Tsukuba Ibaraki Japan Univ Brasilia Brasilia DF Brazil Univ Stirling Stirling Scotland

ISBN: (纸本)9781450392372

The performance of multiobjective algorithms varies across problems, making it hard to develop new algorithms or apply existing ones to new problems. To simplify the development and application of new multiobjective algorithms, there has been an increasing interest in their automatic design from component parts. These automatically designed metaheuristics can outperform their human-developed counterparts. However, it is still uncertain what are the most influential components leading to their performance improvement. This study introduces a new methodology to investigate the effects of the final configuration of an automatically designed algorithm. We apply this methodology to a well-performing Multiobjective Evolutionary algorithm Based on Decomposition (MOEA/D) designed by the irace package on nine constrained problems. We then contrast the impact of the algorithm components in terms of their Search Trajectory Networks (STNs), the diversity of the population, and the hypervolume. Our results indicate that the most influential components were the restart and update strategies, with higher increments in performance and more distinct metric values. Also, their relative influence depends on the problem difficulty: not using the restart strategy was more influential in problems where MOEA/D performs better;while the update strategy was more influential in problems where MOEA/D performs the worst.

关键词： algorithm analysis continuous optimization automatic algorithm configuration multi-objective optimization

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Capping methods for the automatic configuration of optimization algorithms

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COMPUTERS & OPERATIONS RESEARCH 2022年 139卷 1页

作者： de Souza, Marcelo Ritt, Marcus Lopez-Ibanez, Manuel Santa Catarina State Univ Dept Software Engn Florianopolis SC Brazil Univ Fed Rio Grande do Sul Inst Informat Porto Alegre RS Brazil Univ Malaga Sch Comp Sci Malaga Spain Univ Manchester Alliance Manchester Business Sch Manchester Lancs England

automatic configuration techniques are widely and successfully used to find good parameter settings for optimization algorithms. configuration is costly, because it is necessary to evaluate many configurations on different instances. For decision problems, when the objective is to minimize the running time of the algorithm, many configurators implement capping methods to discard poor configurations early. Such methods are not directly applicable to optimization problems, when the objective is to optimize the cost of the best solution found, given a predefined running time limit. We propose new capping methods for the automatic configuration of optimization algorithms. They use the previous executions to determine a performance envelope, which is used to evaluate new executions and cap those that do not satisfy the envelope conditions. We integrate the capping methods into the irace configurator and evaluate them on different optimization scenarios. Our results show that the proposed methods can save from about 5% to 78% of the configuration effort, while finding configurations of the same quality. Based on the computational analysis, we identify two conservative and two aggressive methods, that save an average of about 20% and 45% of the configuration effort, respectively. We also provide evidence that capping can help to better use the available budget in scenarios with a configuration time limit.

关键词： automatic algorithm configuration Capping methods Optimization algorithms Parameter tuning

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MPILS: An automatic Tuner for MILP Solvers

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COMPUTERS & OPERATIONS RESEARCH 2023年第1期159卷

作者： Himmich, Ilyas Raqabi, El Mehdi Er El Hachemi, Nizar El Hallaoui, Issmail Metrane, Abdelmoutalib Soumis, Francois Polytech Montreal Dept Math & Genie Ind CP 6079Succ Ctr Ville Montreal PQ H3C 3A7 Canada Dept Math & Genie Ind GERAD CP 6079Succ Ctr Ville Montreal PQ H3C 3A7 Canada Natl Inst Stat & Appl Econ Rabat Morocco Mohammed V Univ Mohammadia Sch Engineers Rabat Morocco Cadi Ayyad Univ FST Marrakech Marrakech Morocco Mohammed VI Polytech Univ Africa Business Sch Business Analyt Value Network Team Ben Guerir Morocco

The parameter configuration problem consists of finding a parameter configuration that gives a particular algorithm the best performance. This paper introduces a new multi-phase tuner based on the iterated local search meta-heuristic. This tuner addresses the parameter configuration problem for deterministic MILP solvers that are used to solve challenging industrial optimization problems. Further, the proposed tuner offers a new search strategy based on three ideas. First, instead of tuning in the entire configuration space induced by the parameter set, the multi-phase tuner focuses on a small parameter pool that is dynamically enriched with new promising parameters. Second, it leverages the gathered knowledge during the search using statistical learning to forbid less promising parameter combinations. Third, it tunes on a single instance provided by earlier clustering of MILP instances. A computational study on the widely-used commercial solver CPLEX with instances from the MIPLIB library and a real large-scale optimization problem highlights the promising potential of the tuner.

关键词： Parameter configuration problem automatic algorithm configuration MILP solvers Metaheuristics Machine learning CPLEX

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Flowshop NEH-Based Heuristic Recommendation 1

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21st European Conference on Evolutionary Computation in Combinatorial Optimization (EvoCOP)

作者： Pavelski, Lucas Marcondes Kessaci, Marie-Eleonore Delgado, Myriam Univ Tecnol Fed Parana Av Sete Setembro 3165 BR-80230901 Curitiba Brazil Univ Lille Cent Lille CNRS UMR CRIStAL 9189 F-59000 Lille France

ISBN: (数字)9783030729042

ISBN: (纸本)9783030729035;9783030729042

Flowshop problems (FSPs) have many variants and a broad set of heuristics proposed to solve them. Choosing the best heuristic and its parameters for a given FSP instance can be very challenging for practitioners. Per-instance algorithm configuration (PIAC) approaches aim at recommending the best algorithm configuration for a particular instance problem. This paper presents a PIAC methodology for building models to automatically configure the Nawaz, Encore, and Ham (NEH) algorithm which proved to be a good choice in most FSP variants (especially when they are used to provide initial solutions). We use irace to build the performance dataset (problem features. algorithm configuration), while training Decision Tree and Random Forest models to recommend NEH configurations on unseen problems of the test set. Results show that the recommended heuristics have good performance, especially those by random forest models considering parameter dependencies.

关键词： Flowshop Heuristics automatic algorithm configuration Parameter dependencies Machine learning

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automatic generation of iterated greedy algorithms for the non-permutation flow shop scheduling problem with total completion time minimization

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COMPUTERS & INDUSTRIAL ENGINEERING 2022年 163卷

作者： Brum, Artur Ruiz, Ruben Ritt, Marcus Univ Fed Rio Grande do Sul Inst Informat Av Bento Goncalves 9500 BR-91501970 Porto Alegre RS Brazil Univ Politecn Valencia Inst Tecnol Informat Grp Sistemas Optimizat Aplicada Ciudad Politecn Innovac Acc BEdif 8GCamino Vera S-N Valencia 46021 Spain

In this paper we address the non-permutation flow shop scheduling problem, a more general variant of the flow shop problem in which the machines can have different sequences of jobs. We aim to minimize the total completion time. We propose a template to generate iterated greedy algorithms, and use an automatic algorithm configuration to obtain efficient methods. This is the first automated approach in the literature for the non-permutation flow shop scheduling problem. The algorithms start by building a high-quality permutation solution, which is then improved during a second phase that generates non-permutation solutions by changing the job order on some machines. The obtained algorithms are evaluated against two well-known benchmarks from the literature. The results show that they can find better schedules than the state-of-the-art methods for both the permutation and non-permutation flow shop scheduling problems in comparable experimental conditions, as evidenced by comprehensive computational and statistical testing. We conclude that using non-permutation schedules is a viable alternative to reduce the total completion time that production managers should consider.

关键词： Non-permutation flow shop Total completion time Iterated greedy automatic algorithm configuration

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