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automatic design of hybrid stochastic local search algorithms for permutation flowshop problems with additional constraints

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OPERATIONS RESEARCH PERSPECTIVES 2021年 8卷

作者： Pagnozzi, Federico Stutzle, Thomas Univ Libre Bruxelles ULB IRIDIA 50 Av F RooseveltCP 194-6 B-1050 Brussels Belgium

automatic design of stochastic local search algorithms has been shown to be very effective in generating algorithms for the permutation flowshop problem for the most studied objectives including makespan, flowtime and total tardiness. The automatic design system uses a configuration tool to combine algorithmic components following a set of rules defined as a context-free grammar. In this paper we use the same system to tackle two of the most studied additional constraints for these objectives: sequence dependent setup times and no-idle constraint. Additional components have been added to adapt the system to the new problems while keeping intact the grammar structure and the experimental setup. The experiments show that the generated algorithms outperform the state of the art in each case.

关键词： Combinatorial optimization Stochastic local search algorithms automatic algorithm design Permutation flowshop problem No-idle Sequence dependent setup times

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automatically designing State-of-the-Art Multi- and Many-Objective Evolutionary algorithms

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EVOLUTIONARY COMPUTATION 2020年第2期28卷 195-226页

作者： Bezerra, Leonardo C. T. Lopez-Ibanez, Manuel Stutzle, Thomas Univ Fed Rio Grande do Norte Inst Metropole Digital IMD Natal RN Brazil Univ Manchester Alliance Manchester Business Sch Manchester Lancs England Univ Libre Bruxelles IRIDIA CoDE Brussels Belgium

A recent comparison of well-established multiobjective evolutionary algorithms (MOEAs) has helped better identify the current state-of-the-art by considering (i) parameter tuning through automatic configuration, (ii) a wide range of different setups, and (iii) various performance metrics. Here, we automatically devise MOEAs with verified state-of-the-art performance for multi- and many-objective continuous optimization. Our work is based on two main considerations. The first is that high-performing algorithms can be obtained from a configurable algorithmic framework in an automated way. The second is that multiple performance metrics may be required to guide this automatic design process. In the first part of this work, we extend our previously proposed algorithmic framework, increasing the number of MOEAs, underlying evolutionary algorithms, and search paradigms that it comprises. These components can be combined following a general MOEA template, and an automatic configuration method is used to instantiate high-performing MOEA designs that optimize a given performance metric and present state-of-the-art performance. In the second part, we propose a multiobjective formulation for the automatic MOEA design, which proves critical for the context of many-objective optimization due to the disagreement of established performance metrics. Our proposed formulation leads to an automatically designed MOEA that presents state-of-the-art performance according to a set of metrics, rather than a single one.

关键词： Multiobjective optimization evolutionary algorithms automatic algorithm design

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automatic design of hybrid stochastic local search algorithms for permutation flowshop problems

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2019年第2期276卷 409-421页

作者： Pagnozzi, Federico Stutzle, Thomas ULB IRIDIA CP 194-6Av F Roosevelt 50 B-1050 Brussels Belgium

Stochastic local search methods are at the core of many effective heuristics for tackling different permutation flowshop problems (PFSPs). Usually, such algorithms require a careful, manual algorithm engineering effort to reach high performance. An alternative to the manual algorithm engineering is the automated design of effective SLS algorithms through building flexible algorithm frameworks and using automatic algorithm configuration techniques to instantiate high-performing algorithms. In this paper, we automatically generate new high-performing algorithms for some of the most widely studied variants of the PFSP. More in detail, we (i) developed a new algorithm framework, EMILI, that implements algorithm-specific and problem-specific building blocks;(ii) define the rules of how to compose algorithms from the building blocks;and (iii) employ an automatic algorithm configuration tool to search for high performing algorithm configurations. With these ingredients, we automatically generate algorithms for the PFSP with the objectives makespan, total completion time and total tardiness, which outperform the best algorithms obtained by a manual algorithm engineering process. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Scheduling Stochastic local search automatic algorithm design

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Revisiting simulated annealing: A component-based analysis

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COMPUTERS & OPERATIONS RESEARCH 2019年 104卷 191-206页

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

Simulated Annealing (SA) is one of the oldest metaheuristics and has been adapted to solve many combinatorial optimization problems. Over the years, many authors have proposed both general and problem-specific improvements and variants of SA. We propose to accumulate this knowledge into automatically configurable, algorithmic frameworks so that for new applications that wealth of alternative algorithmic components is directly available for the algorithm designer without further manual intervention. Here, we describe SA as an ensemble of algorithmic components, and describe SA variants from the literature within these components. We show the advantages of our proposal by (i) implementing existing algorithmic components of variants of SA, (ii) studying SA algorithms proposed in the literature, (iii) improving SA performance by automatically designing new state-of-the-art SA implementations and (iv) studying the role and impact of the algorithmic components based on experimental data. Our experiments consider three common combinatorial optimization problems, the quadratic assignment problem and two variants of the permutation flow shop problem. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Simulated annealing Metaheuristics Stochastic local search automatic algorithm design Experimental analysis

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Offline automatic Parameter Tuning of MOEA/D Using Genetic algorithm

Offline Automatic Parameter Tuning of MOEA/D Using Genetic A...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Pang, Lie Meng Ishibuchi, Hisao Shang, Ke Southern Univ Sci & Technol Univ Key Lab Evolving Intelligent Syst Guangdong Dept Comp Sci & Engn Shenzhen Key Lab Computat Intelligence Shenzhen Peoples R China

ISBN: (纸本)9781728124858

The multiobjective evolutionary algorithm based on decomposition (MOEA/D) is one of the favorite algorithms in the evolutionary computation community. In this paper, a genetic algorithm is used to automatically tune the parameters for MOEA/D in an offline manner. We consider a version of MOEA/D with a normalization mechanism and two neighborhood structures (for mating and replacement). Our experimental results show that the automatically obtained implementation of MOEA/D outperforms MOEA/D with the default settings in their applications to the DTLZ and WFG test suites. The obtained implementation for each test problem also allows us to discover some potentially good parameter values that can lead to the performance improvement of MOEA/D on certain test problems.

关键词： automatic algorithm design hyper-heuristics MOEA/D multiobjective optimization offline parameter tuning

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automatic design of Evolutionary algorithms based on Entropy Triggers

Automatic Design of Evolutionary Algorithms based on Entropy...

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IEEE Congress on Evolutionary Computation (IEEE CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： da Silva, Guilherme Ribeiro Basgalupp, Marcio P. Lorena, Ana Carolina Univ Fed Sao Paulo Inst Ciencia & Tecnol Sao Jose Dos Campos SP Brazil

ISBN: (纸本)9781509060177

The field of automatic algorithm design has received increasing attention in recent years. From a multitude of available algorithms, a researcher can effectively design a new one customized to his/her own problem. For this, hyper-heuristics techniques have proven to be useful. Their main objective is to search in the space of heuristics rather than in the problem solution space. The present paper proposes a hyper-heuristic for the automatic design of evolutionary algorithms supported by the use of an entropy metric. This metric is used as a trigger mechanism for switching between the algorithms components, aiding the formation of the new hybrid algorithm.

关键词： hyper-heuristics automatic algorithm design grammatical-evolution automatic algorithm selection

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Reacting and Adapting to the Environment: designing Autonomous Methods for Multi-Objective Combinatorial Optimisation

Reacting and Adapting to the Environment: Designing Autonomo...

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作者： Aymeric Blot Universite de Lille

学位级别：博士

Large-scale optimisation problems are usually hard to solve optimally. Approximation algorithms such as metaheuristics, able to quickly find sub-optimal solutions, are often preferred. This thesis focuses on multi-objective local search (MOLS) algorithms, metaheuristics able to deal with the simultaneous optimisation of multiple criteria. As many algorithms, metaheuristics expose many parameters that significantly impact their performance. These parameters can be either predicted and set before the execution of the algorithm, or dynamically modified during the execution itself. While in the last decade many advances have been made on the automatic design of algorithms, the great majority of them only deal with single-objective algorithms and the optimisation of a single performance indicator such as the algorithm running time or the final solution quality. In this thesis, we investigate the relations between automatic algorithm design and multi-objective optimisation, with an application on MOLS algorithms. We first review possible MOLS strategies ans parameters and present a general, highly configurable, MOLS framework. We also propose MO-ParamILS, an automatic configurator specifically designed to deal with multiple performance indicators. Then, we conduct several studies on the automatic offline design of MOLS algorithms on multiple combinatorial bi-objective problems. Finally, we discuss two online extensions of classical algorithm configuration: first the integration of parameter control mechanisms, to benefit from having multiple configuration predictions; then the use of configuration schedules, to sequentially use multiple configurations.

关键词： automatic algorithm design multi-objective optimisation combinat-orial optimisation metaheuristics local search algorithms

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Interactive evolutionary computation with minimum fitness evaluation requirement and offline algorithm design

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SPRINGERPLUS 2016年第1期5卷 192页

作者： Ishibuchi, Hisao Sudo, Takahiko Nojima, Yusuke Osaka Prefecture Univ Grad Sch Engn Dept Comp Sci & Intelligent Syst Naka Ku 1-1 Gakuen Cho Sakai Osaka 5998531 Japan

In interactive evolutionary computation (IEC), each solution is evaluated by a human user. Usually the total number of examined solutions is very small. In some applications such as hearing aid design and music composition, only a single solution can be evaluated at a time by a human user. Moreover, accurate and precise numerical evaluation is difficult. Based on these considerations, we formulated an IEC model with the minimum requirement for fitness evaluation ability of human users under the following assumptions: They can evaluate only a single solution at a time, they can memorize only a single previous solution they have just evaluated, their evaluation result on the current solution is whether it is better than the previous one or not, and the best solution among the evaluated ones should be identified after a pre-specified number of evaluations. In this paper, we first explain our IEC model in detail. Next we propose a (mu + 1) ES-style algorithm for our IEC model. Then we propose an offline meta-level approach to automated algorithm design for our IEC model. The main feature of our approach is the use of a different mechanism (e.g., mutation, crossover, random initialization) to generate each solution to be evaluated. Through computational experiments on test problems, our approach is compared with the (mu + 1) ES-style algorithm where a solution generation mechanism is pre-specified and fixed throughout the execution of the algorithm.

关键词： Interactive evolutionary computation Interactive algorithms automatic algorithm design Meta-level evolutionary algorithms

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automatic design of algorithms for the traveling salesman problem

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COGENT ENGINEERING 2016年第1期3卷 Article: 1255165页

作者： Loyola, Cristian Sepulveda, Mauricio Solar, Mauricio Lopez, Pierre Parada, Victor Univ Santiago Chile Dept Ingn Informat 3659 Ave Ecuador Santiago Chile Univ Tecn Federico Santa Maria Dept Informat Campus San JoaquinAve Vicuna Mackenna 3939 Santiago Chile Univ Toulouse CNRS LAAS Toulouse France

The automatic generation of procedures for combinatorial optimization problems is emerging as a new alternative to address the hardest problems of this class. One of these problems still offering great computational difficulty is the traveling salesman problem. Its simple presentation masks the great difficulty that exists when solving it numerically. The results obtained so far for this problem are based on the hybridization of known heuristics. However, there is still a need for an experimental breakthrough in the study of all of the possible combinations of heuristics, which represents a huge search space. In this paper, we explore this space using evolutionary computing to automatically design new algorithms for the problem. We carried out a computational experiment to produce the algorithms that not only are competitive with some of the existing heuristics but that also contain several novel structures that directly influence performance.

关键词： traveling salesman problem automatic algorithm design heuristics hyperheuristics genetic programming evolutionary computing

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Evolutionary design of Decision-Tree algorithms Tailored to Microarray Gene Expression Data Sets

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2014年第6期18卷 873-892页

作者： Barros, Rodrigo C. Basgalupp, Marcio P. Freitas, Alex A. de Carvalho, Andre C. P. L. F. Pontificia Univ Catolica Rio Grande do Sul Fac Informat BR-90619900 Porto Alegre RS Brazil Univ Sao Paulo Dept Comp Sci BR-13566590 Sao Carlos SP Brazil Univ Fed Sao Paulo Inst Ciencia & Tecnol BR-12231280 Sao Jose Dos Campos Brazil Univ Kent Dept Comp Sci Canterbury CT2 7NF Kent England

Decision-tree induction algorithms are widely used in machine learning applications in which the goal is to extract knowledge from data and present it in a graphically intuitive way. The most successful strategy for inducing decision trees is the greedy top-down recursive approach, which has been continuously improved by researchers over the past 40 years. In this paper, we propose a paradigm shift in the research of decision trees: instead of proposing a new manually designed method for inducing decision trees, we propose automatically designing decision-tree induction algorithms tailored to a specific type of classification data set (or application domain). Following recent breakthroughs in the automatic design of machine learning algorithms, we propose a hyper-heuristic evolutionary algorithm called hyper-heuristic evolutionary algorithm for designing decision-tree algorithms (HEAD-DT) that evolves design components of top-down decision-tree induction algorithms. By the end of the evolution, we expect HEAD-DT to generate a new and possibly better decision-tree algorithm for a given application domain. We perform extensive experiments in 35 real-world microarray gene expression data sets to assess the performance of HEAD-DT, and compare it with very well known decision-tree algorithms such as C4.5, CART, and REPTree. Results show that HEAD-DT is capable of generating algorithms that significantly outperform the baseline manually designed decision-tree algorithms regarding predictive accuracy and F-measure.

关键词： automatic algorithm design classification decision trees evolutionary algorithms hyper-heuristics machine learning

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