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automatic algorithm configuration for the Permutation Flow Shop Scheduling Problem Minimizing Total Completion Time 1

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18th European Conference on Evolutionary Computation in Combinatorial Optimization (EvoCOP)

作者： Brum, Artur Ritt, Marcus Univ Fed Rio Grande Sul UFRGS Inst Informat Porto Alegre RS Brazil

ISBN: (数字)9783319774497

ISBN: (纸本)9783319774497;9783319774480

automatic algorithm configuration aims to automate the often time-consuming task of designing and evaluating search methods. We address the permutation flow shop scheduling problem minimizing total completion time with a context-free grammar that defines how algorithmic components can be combined to form a full heuristic search method. We implement components from various works from the literature, including several local search procedures. The search space defined by the grammar is explored with a racing-based strategy and the algorithms obtained are compared to the state of the art.

关键词： automatic algorithm configuration Iterated greedy algorithm Iterated local search Flow shop scheduling problem Total completion time

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Grammar-based generation of stochastic local search heuristics through automatic algorithm configuration tools

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COMPUTERS & OPERATIONS RESEARCH 2014年 51卷 190-199页

作者： Mascia, Franco Lopez-Ibanez, Manuel Dubois-Lacoste, Jeremie Stutzle, Thomas Univ Libre Bruxelles IRIDIA CoDE B-1050 Brussels Belgium

Several grammar-based genetic programming algorithms have been proposed in the literature to automatically generate heuristics for hard optimization problems. These approaches specify the algorithmic building blocks and the way in which they can be combined in a grammar;the best heuristic for the problem being tackled is found by an evolutionary algorithm that searches in the algorithm design space defined by the grammar. In this work, we propose a novel representation of the grammar by a sequence of categorical, integer, and real-valued parameters. We then use a tool for automatic algorithm configuration to search for the best algorithm for the problem at hand. Our experimental evaluation on the one-dimensional bin packing problem and the permutation flowshop problem with weighted tardiness objective shows that the proposed approach produces better algorithms than grammatical evolution, a well-established variant of grammar-based genetic programming. The reasons behind such improvement lie both in the representation proposed and in the method used to search the algorithm design space. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Heuristics Grammatical evolution automatic algorithm configuration Bin packing Flowshop scheduling

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Unbalanced budget distribution for automatic algorithm configuration

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SOFT COMPUTING 2022年第3期26卷 1315-1330页

作者： Ghambari, Soheila Rakhshani, Hojjat Lepagnot, Julien Jourdan, Laetitia Idoumghar, Lhassane Univ Haute Alsace IRIMAS UR 7499 F-68100 Mulhouse France Univ Lille Cent Lille CNRS CRIStALUMR 9189 Lille France

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 is an element of+) 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.

关键词： automatic algorithm configuration Multi-objective optimization Bandit-based approaches Deep learning

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Latin Hypercube Designs with Branching and Nested Factors for Initialization of automatic algorithm configuration

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EVOLUTIONARY COMPUTATION 2019年第1期27卷 129-145页

作者： Wessing, Simon Lopez-Ibanez, Manuel Tech Univ Dortmund Comp Sci Dept Dortmund Germany Univ Manchester Alliance Manchester Business Sch Manchester Lancs England

The configuration of algorithms is a laborious and difficult process. Thus, it isadvisable to automate this task by using appropriate automatic configurationmethods. The irace method is among the most widely used in theliterature. By default, irace initializes its search process via uniformsampling of algorithm configurations. Although better initialization methodsexist in the literature, the mixed-variable (numerical and categorical) natureof typical parameter spaces and the presence of conditional parameters make mostof the methods not applicable in practice. Here, we present an improvedinitialization method that overcomes these limitations by employing conceptsfrom the design and analysis of computer experiments with branching and nestedfactors. Our results show that this initialization method is not only better, insome scenarios, than the uniform sampling used by the current version of irace, but also better than other initializationmethods present in other automatic configuration methods.

关键词： automatic algorithm configuration sampling branching and nested designs racing

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Effect of transformations of numerical parameters in automatic algorithm configuration

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OPTIMIZATION LETTERS 2018年第8期12卷 1741-1753页

作者： Franzin, Alberto Caceres, Leslie Perez Stutzle, Thomas ULB IRIDIA 50 Av F Roosevelt B-1050 Brussels Belgium

We study the impact of altering the sampling space of parameters in automatic algorithm configurators. We show that a proper transformation can strongly improve the convergence towards better configurations;at the same time, biases about good parameter values, possibly based on misleading prior knowledge, may lead to wrong choices in the transformations and be detrimental for the configuration process. To emphasize the impact of the transformations, we initially study their effect on configuration tasks with a single parameter in different experimental settings. We also propose a mechanism for how to adapt towards an appropriate transformation and give exemplary experimental results of that scheme.

关键词： automatic algorithm configuration Numerical parameters Parameter tuning Parameter transformation

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The irace package: Iterated racing for automatic algorithm configuration

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OPERATIONS RESEARCH PERSPECTIVES 2016年 3卷 43-58页

作者： Lopez-Ibanez, Manuel Dubois-Lacoste, Jeremie Caceres, Leslie Perez Birattari, Mauro Stutzle, Thomas Univ Manchester Alliance Manchester Business Sch Manchester M13 9PL Lancs England ULB IRIDIA CP 194-6Av Roosevelt 50 B-1050 Brussels Belgium

Modern optimization algorithms typically require the setting of a large number of parameters to optimize their performance. The immediate goal of automatic algorithm configuration is to find, automatically, the best parameter settings of an optimizer. Ultimately, automatic algorithm configuration has the potential to lead to new design paradigms for optimization software. The irace package is a software package that implements a number of automatic configuration procedures. In particular, it offers iterated racing procedures, which have been used successfully to automatically configure various state-of-the-art algorithms. The iterated racing procedures implemented in irace include the iterated F-race algorithm and several extensions and improvements over it. In this paper, we describe the rationale underlying the iterated racing procedures and introduce a number of recent extensions. Among these, we introduce a restart mechanism to avoid premature convergence, the use of truncated sampling distributions to handle correctly parameter bounds, and an elitist racing procedure for ensuring that the best configurations returned are also those evaluated in the highest number of training instances. We experimentally evaluate the most recent version of irace and demonstrate with a number of example applications the use and potential of irace, in particular, and automatic algorithm configuration, in general. (C) 2016 The Authors. Published by Elsevier Ltd.

关键词： automatic algorithm configuration Racing Parameter tuning

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Exploration of Metaheuristics through automatic algorithm configuration Techniques and algorithmic Frameworks

Exploration of Metaheuristics through Automatic Algorithm Co...

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Genetic and Evolutionary Computation Conference (GECCO)

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

ISBN: (纸本)9781450343237

In this paper(1) we argue that flexible algorithm frameworks can be useful to capture the wide variety of algorithmic components for heuristic algorithms and serve as basic experimental frameworks. One of the utilities is that they can implement the wide variety of different algorithm components and their alternative choices for single stochastic local search methods and we are currently extending existing frameworks in that direction. We exemplify this approach considering the example of Simulated Annealing (SA). In fact, a wide variety of design choices of SA algorithms has been proposed in the literature and algorithm frameworks may (i) simply collect potentially all available choices, (ii) provide a tool for the experimental analysis of specific algorithms and component choices, and (iii) allow the generation of new algorithm variants by combining exisiting components in new ways. We show some limited computational experiments that show the benefit of tuning in this context and the way conclusions on the performance of algorithms are altered in this way.

关键词： Metaheuristics Simulated Annealing algorithm Design automatic algorithm configuration QAP algorithm Framework

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Exploring variable neighborhood search for automatic algorithm configuration

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Electronic Notes in Discrete Mathematics 2017年 58卷 167-174页

作者： Pérez Cáceres, Leslie Stützle, Thomas IRIDIA Université Libre de Bruxelles CP 194/6 Av. F. Roosevelt 50 B-1050 Belgium

Methods for automatic algorithm configuration integrate some search mechanism for generating candidate algorithm configurations and mechanisms for handling the stochasticity of the algorithm configuration problem. One popular algorithm configurator is ParamILS, which searches the configuration space using an iterated local search algorithm. In our research, we explore variable neighborhood search mechanisms as an alternative for the one-exchange neighborhood that is searched in the local search phase of ParamILS. In this article, we explore a reduced variable neighborhood search for automatic configuration. Our experimental results are promising and indicate directions for extending our work. © 2017 Elsevier B.V.

关键词： automatic algorithm configuration ParamILS Variable neighborhood search

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automatic configuration of Genetic algorithm for the Optimization of Electricity Market Participation Using Sequential Model algorithm configuration 21st

Automatic Configuration of Genetic Algorithm for the Optimiz...

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21st EPIA Conference on Artificial Intelligence (EPIA)

作者： Oliveira, Vitor Pinto, Tiago Faia, Ricardo Veiga, Bruno Soares, Joao Romero, Ruben Vale, Zita Inst Super Engn Porto GECAD Porto Portugal Univ Tras Os Montes & Alto Douro Vila Real Portugal INESC TEC Vila Real Portugal Sao Paulo State Univ Dept Elect Engn Ilha Solteira SP Brazil

ISBN: (纸本)9783031164743;9783031164736

Complex optimization problems are often associated to large search spaces and consequent prohibitive execution times in finding the optimal results. This is especially relevant when dealing with dynamic real problems, such as those in the field of power and energy systems. Solving this type of problems requires new models that are able to find near-optimal solutions in acceptable times, such as metaheuristic optimization algorithms. The performance of these algorithms is, however, hugely dependent on their correct tuning, including their configuration and parametrization. This is an arduous task, usually done through exhaustive experimentation. This paper contributes to overcome this challenge by proposing the application of sequential model algorithm configuration using Bayesian optimization with Gaussian process and Monte Carlo Markov Chain for the automatic configuration of a genetic algorithm. Results from the application of this model to an electricity market participation optimization problem show that the genetic algorithm automatic configuration enables identifying the ideal tuning of the model, reaching better results when compared to a manual configuration, in similar execution times.

关键词： automatic algorithm configuration Electricity markets Genetic algorithm Metaheuristic optimization Portfolio optimization

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Online algorithm configuration for differential evolution algorithm

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APPLIED INTELLIGENCE 2022年第8期52卷 9193-9211页

作者： Huang, Changwu Bai, Hao Yao, Xin Southern Univ Sci & Technol Dept Comp Sci & Engn Guangdong Prov Key Lab Brain Inspired Intelligent Shenzhen 518055 Peoples R China INSA Rouen Normandie Lab Mech Normandy LMN F-76000 Rouen France

The performance of evolutionary algorithms (EAs) is strongly affected by their configurations. Thus, algorithm configuration (AC) problem, that is, to properly set algorithm's configuration, including the operators and parameter values for maximizing the algorithm's performance on given problem(s) is an essential and challenging task in the design and application of EAs. In this paper, an online algorithm configuration (OAC) approach is proposed for differential evolution (DE) algorithm to adapt its configuration in a data-driven way. In our proposed OAC, the multi-armed bandit algorithm is adopted to select trial vector generation strategies for DE, and the kernel density estimation method is used to adapt the associated control parameters during the evolutionary search process. The performance of DE algorithm using the proposed OAC (OAC-DE) is evaluated on a benchmark set of 30 bound-constrained numerical optimization problems and compared with several adaptive DE variants. Besides, the influence of OAC's hyper-parameter on its performance is analyzed. The comparison results show OAC-DE achieves better average performance than the compared algorithms, which validates the effectiveness of the proposed OAC. The sensitivity analysis indicates that the hyper-parameter of OAC has little impact on OAC-DE's performance.

关键词： automatic algorithm configuration Differential evolution algorithm Adaptive parameter control Multi-armed bandit Kernel density estimation Machine learning

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