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11th Brazilian Conference on Intelligent Systems (BRACIS)

作者： Pavelski, Lucas Marcondes Delgado, Myriam Regattieri Kessaci, Marie-Eleonore Fed Univ Technol Parana UTFPR Grad Program Elect & Comp Engn CPGEI Curitiba Parana Brazil Univ Lille CRIStAL CNRS Cent LilleUMR 9189 F-59000 Lille France

ISBN: (纸本)9783031216855;9783031216862

Per-instance algorithm configuration (PIAC) is an important task in which, given a base problem instance, a recommendation model indicates the best configuration to solve it. Whereas typical Automated algorithm configuration (AAC) prescribes configurations for a fixed set of instances, in PIAC, a model is trained using problem features and past experience in solving the base problem and is tested individually on new instances. This work proposes a novel formulation for PIAC called Multi-Objective Automated algorithm configuration based on Decomposition (MOAAC/D). Unlike other PIAC approaches, it decomposes the problem space by associating different instance sets with each objective. Using a particular implementation called iMOEA/D, we show an efficient search with irace as a local search of the Multi-objective algorithm based on Decomposition (MOEA/D). Experiments on 6,480 base problem instances show that our proposal is general and performs well on flowshop, a well-studied combinatorial optimization problem with many variants addressed as real-world applications. During the training phase, iMOEA/D searches for good configurations by tuning Iterated Local Search with Iterated Greedy operators. The testing phase uses the generated Pareto front to recommend parameters for new flowshop instances. The results show that the proposed approach outperforms a random selection baseline, a generalist solution provided by irace, and is an alternative to a meta-learning-based approach. We believe the proposed MOAAC/D formulation has the potential to open up a novel research area: multi-objective tuners capable of providing specialist and generalist configurations simultaneously.

关键词： algorithm configuration Multi-objective optimization Flowshop problem

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Configuring Mixed-Integer Programming Solvers for Large-Scale Instances

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Operations Research Forum 2024年第2期5卷 48页

作者： Kemminer, Robin Lange, Jannick Kempkes, Jens Peter Tierney, Kevin Weiß, Dimitri OPTANO GmbH Technologiepark 18 NRW Paderborn 33100 Germany Decision and Operation Technologies Bielefeld University Universitätsstraße 25 NRW Bielefeld 33615 Germany

algorithm configuration techniques automatically search for parameters of solvers and algorithms that provide minimal runtime or maximal solution quality on specified instance sets. Mixed-integer programming (MIP) solvers pose a particular challenge for algorithm configurators due to the difficulty of finding optimal, or even feasible, solutions on the large-scale problems commonly found in practice. We introduce the OPTANO algorithm Tuner (OAT) to find configurations for MIP solvers and other optimization algorithms. We present and evaluate several critical components of OAT for solving MIPs in particular and show that OAT can find configurations that significantly improve the solution time of MIPs on two different datasets. © The Author(s) 2024.

关键词： algorithm configuration Large-scale problem instances Mixed-integer programming

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Analyzing the Impact of Undersampling on the Benchmarking and configuration of Evolutionary algorithms

Analyzing the Impact of Undersampling on the Benchmarking an...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Vermetten, Diederick Wang, Hao Lopez-Ibanez, Manuel Doerr, Carola Back, Thomas Leiden Inst Adv Comp Sci Leiden Netherlands Univ Malaga Sch Comp Sci & Engn Malaga Spain Sorbonne Univ CNRS LIP6 Paris France

ISBN: (纸本)9781450392372

The stochastic nature of iterative optimization heuristics leads to inherently noisy performance measurements. Since these measurements are often gathered once and then used repeatedly, the number of collected samples will have a significant impact on the reliability of algorithm comparisons. We show that care should be taken when making decisions based on limited data. Particularly, we show that the number of runs used in many benchmarking studies, e.g., the default value of 15 suggested by the COCO environment, can be insufficient to reliably rank algorithms on well-known numerical optimization benchmarks. Additionally, methods for automated algorithm configuration are sensitive to insufficient sample sizes. This may result in the configurator choosing a "lucky" but poor-performing configuration despite exploring better ones. We show that relying on mean performance values, as many configurators do, can require a large number of runs to provide accurate comparisons between the considered configurations. Common statistical tests can greatly improve the situation in most cases but not always. We show examples of performance losses of more than 20%, even when using statistical races to dynamically adjust the number of runs, as done by irace. Our results underline the importance of appropriately considering the statistical distribution of performance values.

关键词： Parameter tuning algorithm configuration performance measures evolution strategies

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Sparkle: Toward Accessible Meta-algorithmics for Improving the State of the Art in Solving Challenging Problems

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2022年第6期26卷 1351-1364页

作者： Van der Blom, Koen Hoos, Holger H. H. Luo, Chuan Rook, Jeroen G. G. Leiden Univ Fac Sci NL-2311 EZ Leiden Netherlands Sorbonne Univ LIP6 CNRS F-75005 Paris France Rhein Westfal TH Aachen Dept Comp Sci D-52062 Aachen Germany Univ British Columbia Dept Comp Sci Vancouver BC V6T 1Z4 Canada Beihang Univ Sch Software Beijing 100190 Peoples R China Univ Twente Data Management & Biometr DMB NL-7522 NB Enschede Netherlands

Many fields of computational science advance through improvements in the algorithms used for solving key problems. These advancements are often facilitated by benchmarks and competitions that enable performance comparisons and rankings of solvers. Simultaneously, meta-algorithmic techniques, such as automated algorithm selection and configuration, enable performance improvements by utilizing the complementary strengths of different algorithms or configurable algorithm components. In fact, meta-algorithms have become major drivers in advancing the state of the art in solving many prominent computational problems. However, meta-algorithmic techniques are complex and difficult to use correctly, while their incorrect use may reduce their efficiency, or in extreme cases, even lead to performance losses. Here, we introduce the Sparkle platform, which aims to make meta-algorithmic techniques more accessible to nonexpert users, and to make these techniques more broadly available in the context of competitions, to further enable the assessment and advancement of the true state of the art in solving challenging computational problems. To achieve this, Sparkle implements standard protocols for algorithm selection and configuration that support easy and correct use of these techniques. Following an experiment, Sparkle generates a report containing results, problem instances, algorithms, and other relevant information, for convenient use in scientific publications.

关键词： algorithm configuration algorithm selection benchmarking competitions meta-algorithms software tools

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SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization

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JOURNAL OF MACHINE LEARNING RESEARCH 2022年第1期23卷 1-9页

作者： Lindauer, Marius Eggensperger, Katharina Feurer, Matthias Biedenkapp, Andre Deng, Difan Benjamins, Carolin Ruhkopf, Tim Sass, Rene Hutter, Frank Leibniz Univ Hannover Hannover Germany Univ Freiburg Freiburg Germany Bosch Ctr Artificial Intelligence Renningen Germany

algorithm parameters, in particular hyperparameters of machine learning algorithms, can substantially impact their performance. To support users in determining well-performing hyperparameter configurations for their algorithms, datasets and applications at hand, SMAC3 offers a robust and flexible framework for Bayesian Optimization, which can improve performance within a few evaluations. It offers several facades and pre-sets for typical use cases, such as optimizing hyperparameters, solving low dimensional continuous (artificial) global optimization problems and configuring algorithms to perform well across multiple problem instances. The SMAC3 package is available under a permissive BSD-license at https://***/automl/SMAC3.

关键词： Bayesian Optimization Hyperparameter Optimization Multi-Fidelity Optimization Automated Machine Learning algorithm configuration

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Modular Differential Evolution 23

Modular Differential Evolution

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Vermetten, Diederick Caraffini, Fabio Kononova, Anna V. Back, Thomas Leiden Inst Adv Comp Sci Leiden Netherlands Swansea Univ Swansea Wales

ISBN: (纸本)9798400701191

New contributions in the field of iterative optimisation heuristics are often made in an iterative manner. Novel algorithmic ideas are not proposed in isolation, but usually as extensions of a preexisting algorithm. Although these contributions are often compared to the base algorithm, it is challenging to make fair comparisons between larger sets of algorithm variants. This happens because even small changes in the experimental setup, parameter settings, or implementation details can cause results to become incomparable. Modular algorithms offer a way to overcome these challenges. By implementing the algorithmic modifications into a common framework, many algorithm variants can be compared, while ensuring that implementation details match in all versions. In this work, we propose a version of a modular framework for the popular Differential Evolution (DE) algorithm. We show that this modular approach not only aids in comparison but also allows for a much more detailed exploration of the space of possible DE variants. This is illustrated by showing that tuning the settings of modular DE vastly outperforms a set of commonly used DE versions which have been recreated in our framework. We then investigate these tuned algorithms in detail, highlighting the relation between modules and performance on particular problems.

关键词： Differential Evolution Benchmarking Modular algorithms algorithm configuration

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algorithm Selection across algorithm Configurators A Case Study on Multi-objective Optimization

Algorithm Selection across Algorithm Configurators A Case St...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Misir, Mustafa Duke Kunshan Univ Div Nat & Appl Sci Kunshan Peoples R China

ISBN: (纸本)9781665487689

The present work utilizes algorithm Selection for automatically specifying the parameter tuning method for a given tuning task. The idea of parameter tuning is motivated by the premature algorithm designs and their sub-optimal or poor parameter value choices. The decisions both on the designs and the parameter values are largely given based on the experiences of the developers or the target problem domain experts after a limited number of trials. While the existing tuning approaches tend to offer improvements over the default parameter values for varying algorithms, they can be computationally expensive. Additionally, there is no a single, ultimate parameter tuning strategy. These facts suggest to choose the most effective tuning algorithm for a specific scenario. This study utilizes an existing algorithm Selection system to address this problem. The idea is to allocate potentially the most effective tuning method for a given task instead of relying on a single tuner. On that note, a group of well-known parameter configuration approaches are accommodated as the candidate methods to tune NSGA-II for solving a suite of multi-objective optimization benchmarks, referencing a recent article. The computational analysis revealed that algorithm Selection outperforms those constituent parameter tuning methods when each is used as a standalone manner. Additionally, the dis/-similarity analysis carried on the problem instances / benchmarks give hints on the diversity level of the benchmarks. Furthermore, a similar inspection reported on the parameter tuning procedures show the behavioural resemblance between them.

关键词： algorithm selection algorithm configuration parameter tuning multi-objective optimization

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Non-elitist Selection Can Improve the Performance of Irace 17th

Non-elitist Selection Can Improve the Performance of Irace

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17th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Ye, Furong Vermetten, Diederick Doerr, Carola Back, Thomas Leiden Univ LIACS Leiden Netherlands Sorbonne Univ CNRS LIP6 Paris France

ISBN: (纸本)9783031147142;9783031147135

Modern optimization strategies such as evolutionary algorithms, ant colony algorithms, Bayesian optimization techniques, etc. come with several parameters that steer their behavior during the optimization process. To obtain high-performing algorithm instances, automated algorithm configuration techniques have been developed. One of the most popular tools is irace, which evaluates configurations in sequential races, making use of iterated statistical tests to discard poorly performing configurations. At the end of the race, a set of elite configurations are selected from those survivor configurations that were not discarded, using greedy truncation selection. We study two alternative selection methods: one keeps the best survivor and selects the remaining configurations uniformly at random from the set of survivors, while the other applies entropy to maximize the diversity of the elites. These methods are tested for tuning ant colony optimization algorithms for traveling salesperson problems and the quadratic assignment problem and tuning an exact tree search solver for satisfiability problems. The experimental results show improvement on the tested benchmarks compared to the default selection of irace. In addition, the obtained results indicate that non-elitist can obtain diverse algorithm configurations, which encourages us to explore a wider range of solutions to understand the behavior of algorithms.

关键词： Parameter tuning algorithm configuration Black-box optimization Evolutionary computation

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Reinforcement learning based adaptive metaheuristics 22

Reinforcement learning based adaptive metaheuristics

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Tessari, Michele Iacca, Giovanni Univ Trento Trento Italy

ISBN: (纸本)9781450392686

Parameter adaptation, that is the capability to automatically adjust an algorithm's hyperparameters depending on the problem being faced, is one of the main trends in evolutionary computation applied to numerical optimization. While several handcrafted adaptation policies have been proposed over the years to address this problem, only few attempts have been done so far at apply machine learning to learn such policies. Here, we introduce a general-purpose framework for performing parameter adaptation in continuous-domain metaheuristics based on state-of-the-art reinforcement learning algorithms. We demonstrate the applicability of this framework on two algorithms, namely Covariance Matrix Adaptation Evolution Strategies (CMA-ES) and Differential Evolution (DE), for which we learn, respectively, adaptation policies for the step-size (for CMA-ES), and the scale factor and crossover rate (for DE). We train these policies on a set of 46 benchmark functions at different dimensionalities, with various inputs to the policies, in two settings: one policy per function, and one global policy for all functions. Compared, respectively, to the Cumulative Step-size Adaptation (CSA) policy and to two well-known adaptive DE variants (iDE and jDE), our policies are able to produce competitive results in the majority of cases, especially in the case of DE.

关键词： Evolutionary algorithms Reinforcement Learning Adaptation algorithm configuration

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Few-Shots Parallel algorithm Portfolio Construction via Co-Evolution

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2021年第3期25卷 595-607页

作者： Tang, Ke Liu, Shengcai Yang, Peng Yao, Xin Southern Univ Sci & Technol Dept Comp Sci & Engn Guangdong Key Lab Brain Inspired Intelligent Comp Shenzhen 518055 Peoples R China Southern Univ Sci & Technol Res Inst Trustworthy Autonomous Syst Shenzhen 518055 Peoples R China

Generalization, i.e., the ability of solving problem instances that are not available during the system design and development phase, is a critical goal for intelligent systems. A typical way to achieve good generalization is to learn a model from vast data. In the context of heuristic search, such a paradigm could be implemented as configuring the parameters of a parallel algorithm portfolio (PAP) based on a set of "training" problem instances, which is often referred to as PAP construction. However, compared to the traditional machine learning, PAP construction often suffers from the lack of training instances, and the obtained PAPs may fail to generalize well. This article proposes a novel competitive co-evolution scheme, named co-evolution of parameterized search (CEPS), as a remedy to this challenge. By co-evolving a configuration population and an instance population, CEPS is capable of obtaining generalizable PAPs with few training instances. The advantage of CEPS in improving generalization is analytically shown in this article. Two concrete algorithms, namely, CEPS-TSP and CEPS-VRPSPDTW, are presented for the traveling salesman problem (TSP) and the vehicle routing problem with simultaneous pickup-delivery and time windows (VRPSPDTW), respectively. The experimental results show that CEPS has led to better generalization, and even managed to find new best-known solutions for some instances.

关键词： Training Portfolios Tuning Statistics Sociology Parallel algorithms Machine learning algorithms algorithm configuration automatic parameter tuning co-evolution parallel algorithm portfolios (PAPs) vehicle routing problems

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