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30th IEEE International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Blot, Aymeric Hoos, Holger H. Kessaci, Marie-Eleonore Jourdan, Laetitia Univ Lille CNRS UMR 9189 CRIStAL Lille France Leiden Univ LIACS Leiden Netherlands

ISBN: (纸本)9781538674499

Multi-objective optimisation algorithms expose various parameters that have to be tuned in order to be efficient. Moreover, in multi-objective optimisation, the correlation between objective functions is known to affect search space structure and algorithm performance. Considering the recent success of automatic algorithm configuration (AAC) techniques for the design of multi-objective optimisation algorithms, this raises two interesting questions: what is the impact of correlation between optimisation objectives on (1) the efficacy of different AAC approaches and (2) on the optimised algorithm designs obtained from these automated approaches? In this work, we study these questions for multi-objective local search algorithms (MOLS) for three well-known bi-objective permutation problems, using two single-objective AAC approaches and one multi-objective approach. Our empirical results clearly show that overall, multi-objective AAC is the most effective approach for the automatic configuration of the highly parametric MOLS framework, and that there is no systematic impact of the degree of correlation on the relative performance of the three AAC approaches. We also find that the best-performing configurations differ, depending on the correlation between objectives and the size of the problem instances to be solved, providing further evidence for the usefulness of automatic configuration of multi-objective optimisation algorithms.

关键词： automatic algorithm configuration Multi-objective optimisation Combinatorial optimisation Heuristic algorithms

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automatic configuration of NSGA-II with jMetal and irace 19

Automatic Configuration of NSGA-II with jMetal and irace

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Nebro, Antonio J. Lopez-Ibanez, Manuel Barba-Gonzalez, Cristobal Garcia-Nieto, Jose Univ Malaga Malaga Spain Univ Manchester Manchester Lancs England

ISBN: (纸本)9781450367486

jMetal is a Java-based framework for multi-objective optimization with metaheuristics providing, among other features, a wide set of algorithms that are representative of the state-of-the-art. Although it has become a widely used tool in the area, it lacks support for automatic tuning of algorithm parameter settings, which can prevent obtaining accurate Pareto front approximations, especially for inexperienced users. In this paper, we present a first approach to combine jMetal and irace, a package for automatic algorithm configuration;the NSGA-II is chosen as the target algorithm to be tuned. The goal is to facilitate the combined use of both tools to jMetal users to avoid wasting time in adjusting manually the parameters of the algorithms. Our proposal involves the definition of a new algorithm template for evolutionary algorithms, which allows the flexible composition of multi-objective evolutionary algorithms from a set of configurable components, as well as the generation of configuration files for adjusting the algorithm parameters with irace. To validate our approach, NSGA-II is tuned with a benchmark problems and compared with the same algorithm using standard settings, resulting in a new variant that shows a competitive behavior.

关键词： Multi-objective Optimization Metaheuristics Software Tools automatic algorithm configuration

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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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GPU-Based automatic configuration of Differential Evolution: A Case Study

GPU-Based Automatic Configuration of Differential Evolution:...

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16th Portuguese Conference on Artificial Intelligence (EPIA)

作者： Ugolotti, Roberto Mesejo, Pablo Nashed, Youssef S. G. Cagnoni, Stefano Univ Parma Dept Informat Engn I-43100 Parma Italy

ISBN: (纸本)9783642406690;9783642406683

The performance of an evolutionary algorithm strongly depends on the choice of the parameters which regulate its behavior. In this paper, two evolutionary algorithms (Particle Swarm Optimization and Differential Evolution) are used to find the optimal configuration of parameters for Differential Evolution. We tested our approach on four benchmark functions, and the comparison with an exhaustive search demonstrated its effectiveness. Then, the same method was used to tune the parameters of Differential Evolution in solving a real-world problem: the automatic localization of the hippocampus in histological brain images. The results obtained consistently outperformed the ones achieved using manually-tuned parameters. Thanks to a GPU-based implementation, our tuner is up to 8 times faster than the corresponding sequential version.

关键词： automatic algorithm configuration Global Continuous Optimization Particle Swarm Optimization Differential Evolution GPGPU

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automatic DRAM Subsystem configuration with irace 23

Automatic DRAM Subsystem Configuration with irace

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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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automatic configuration of a Multi-objective Local Search for Imbalanced Classification 16th

Automatic Configuration of a Multi-objective Local Search fo...

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16th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Tari, Sara Hoos, Holger Jacques, Julie Kessaci, Marie-Eleonore Jourdan, Laetitia Univ Lille CNRS UMR 9189 CRIStAL F-59000 Villeneuve Dascq France Leiden Univ LIACS Leiden Netherlands Lille Catholic Univ Fac Gest Econ & Sci Lille France

ISBN: (纸本)9783030581114;9783030581121

MOCA-I is a multi-objective local search algorithm, based on the Pittsburgh representation, that has been formerly designed to solve partial classification problems with imbalanced data. Recently, multi-objective automatic algorithm configuration (MO-AAC) has proven effective in boosting the performance of multi-objective local search algorithms for combinatorial optimization problems. Here, for the first time, we apply MO-ACC to multi-objective local search for rule-based classification problems. Specifically, we present the automatic configuration of MOCA-I (AC-MOCA-I). AC-MOCA-I uses a methodology based on k-fold cross-validation to automatically configure an extended and improved version of MOCA-I. In a series of experiments on well-known datasets from the literature, we consider 183 456 unique configurations for MOCA-I and demonstrate that AC-MOCA-I leads to substantial improvements in performance. Moreover, we investigate the impact of the running time allotted to AC-MOCA-I on performance and the role of specific parameters and components.

关键词： Supervised classification Multi-objective optimization automatic algorithm configuration

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automatic (offline) configuration of algorithms 14

Automatic (offline) configuration of algorithms

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16th Genetic and Evolutionary Computation Conference Companion, GECCO 2014 Companion

作者： Stützle, Thomas López-Ibánez, Manuel Université libre de Bruxelles Brussels Belgium

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Hybrid multi-objective Bayesian estimation of distribution algorithm: a comparative analysis for the multi-objective knapsack problem

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JOURNAL OF HEURISTICS 2018年第1期24卷 25-47页

作者： Martins, Marcella S. R. Delgado, Myriam R. B. S. Luders, Ricardo Santana, Roberto Goncalves, Richard A. de Almeida, Carolina P. Fed Univ Technol Parana UTFPR Ave Sete Setembro 3165 Curitiba Parana Brazil Univ Basque Country UPV EHU San Sebastian 20018 Spain Midwest State Univ Parana UNICTR Guarapuava PR Brazil

Nowadays, a number of metaheuristics have been developed for efficiently solving multi-objective optimization problems. Estimation of distribution algorithms are a special class of metaheuristic that intensively apply probabilistic modeling and, as well as local search methods, are widely used to make the search more efficient. In this paper, we apply a Hybrid Multi-objective Bayesian Estimation of Distribution algorithm (HMOBEDA) in multi and many objective scenarios by modeling the joint probability of decision variables, objectives, and the configuration parameters of an embedded local search (LS). We analyze the benefits of the online configuration of LS parameters by comparing the proposed approach with LS off-line versions using instances of the multi-objective knapsack problem with two to five and eight objectives. HMOBEDA is also compared with five advanced evolutionary methods using the same instances. Results show that HMOBEDA outperforms the other approaches including those with off-line configuration. HMOBEDA not only provides the best value for hypervolume indicator and IGD metric in most of the cases, but it also computes a very diverse solutions set close to the estimated Pareto front.

关键词： Multi-objective estimation of distribution algorithms Probabilistic modeling Local search Hybridization automatic algorithm configuration

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automatic Component-Wise Design of Multiobjective Evolutionary algorithms

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2016年第3期20卷 403-417页

作者： Bezerra, Leonardo C. T. Lopez-Ibanez, Manuel Stutzle, Thomas Univ Libre Bruxelles CoDE IRIDIA Lab B-1050 Brussels Belgium

Multiobjective evolutionary algorithms (MOEAs) are typically proposed, studied, and applied as monolithic blocks with a few numerical parameters that need to be set. Few works have studied how the algorithmic components of these evolutionary algorithms can be classified and combined to produce new algorithmic designs. The motivation for studies of this latter type stem from the development of flexible software frameworks and the usage of automatic algorithm configuration methods to find novel algorithm designs. In this paper, we propose an MOEA template and a new conceptual view of its components that surpasses existing frameworks in both number of algorithms that can be instantiated from the template and flexibility to produce novel algorithmic designs. We empirically demonstrate the flexibility of our proposed framework by automatically designing MOEAs for continuous and combinatorial optimization problems. The automatically designed algorithms are often able to outperform six traditional MOEAs from the literature, even after tuning their numerical parameters.

关键词： automatic algorithm configuration evolutionary algorithms multiobjective optimization permutation flow shop problem (PFSP)

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