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Genetic and Evolutionary Computation Conference (GECCO)

作者： Andova, Andrejaana Cork, Jordan N. Tusar, Tea Filipic, Bogdan Jozef Stefan Inst Jozef Stefan Int Postgrad Sch Ljubljana Slovenia

ISBN: (纸本)9798400704949

A research problem studied extensively in recent years is the prediction of optimization algorithm performance. A common approach is using the landscape features of optimization problems to train machine learning models. These models are then used to predict algorithm performance. Due to the small number of constrained multiobjective optimization problems (CMOPs) available for bench-marking, training a machine learning model to predict algorithm performance is a hard task. To address this issue, this study uses the functions from the bbob and bbob-constrained benchmark problems to generate new CMOPs. These are then used as additional training examples for the machine learning models. Given the large number of generated CMOPs, the experiments in this study are limited to those with two objectives and two variables. The obtained results are promising. Using additional problems in the training phase improves the predictions in half of the defined classification tasks.

关键词： constrained multiobjective optimization exploratory landscape analysis algorithm performance prediction

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Explainable Landscape Analysis in Automated algorithm performance prediction 25th

Explainable Landscape Analysis in Automated Algorithm Perfor...

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25th International Conference on the Applications of Evolutionary Computation (EvoApplications) Held as Part of EvoStar Conference

作者： Trajanov, Risto Dimeski, Stefan Popovski, Martin Korosec, Peter Eftimov, Tome Ss Cyril & Methodius Univ Skopje Fac Comp Sci & Engn Skopje North Macedonia Jozef Stefan Inst Comp Syst Dept Ljubljana Slovenia

ISBN: (纸本)9783031024627;9783031024610

Predicting the performance of an optimization algorithm on a new problem instance is crucial in order to select the most appropriate algorithm for solving that problem instance. For this purpose, recent studies learn a supervised machine learning (ML) model using a set of problem landscape features linked to the performance achieved by the optimization algorithm. However, these models are black-box with the only goal of achieving good predictive performance, without providing explanations which landscape features contribute the most to the prediction of the performance achieved by the optimization algorithm. In this study, we investigate the expressiveness of problem landscape features utilized by different supervised ML models in automated algorithm performance prediction. The experimental results point out that the selection of the supervised ML method is crucial, since different supervised ML regression models utilize the problem landscape features differently and there is no common pattern with regard to which landscape features are the most informative.

关键词： Exploratory landscape analysis algorithm performance prediction Machine learning Feature importance

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Keenness for characterizing continuous optimization problems and predicting differential evolution algorithm performance

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COMPLEX & INTELLIGENT SYSTEMS 2023年第5期9卷 5251-5266页

作者： Li, Yaxin Liang, Jing Yu, Kunjie Yue, Caitong Zhang, Yingjie Zhengzhou Univ Sch Elect & Informat Engn Zhengzhou 450001 Peoples R China

Fitness landscape analysis devotes to characterizing different properties of optimization problems, such as evolvability, sharpness, and neutrality. Although several landscape features have been proposed, only a few of them can be used in practice as predictors of algorithm performance. In this study, the keenness (KEE,) is proposed to characterize the sharpness of the fitness landscape for continuous optimization problems and predict the performance of the differential evolution algorithm. Specifically, a mirror simple random walk algorithm is designed to construct the relevance between the front and back search points in the sampling. The fitness value of each point is replaced by the specific integer. The values in the set of integers with the same circumstance are computed as the feature scalar using the cumulative calculation mechanism. The results of experimental studies in various functions demonstrate the superiority of KEE, in terms of accuracy, reliability, and coverage of samples. Moreover, KEE, has shown excellent practicability in the application of differential evolution algorithm performance prediction for continuous optimization problems. Thus, KEE, is a new landscape feature for fitness landscape analysis of continuous optimization problems and algorithm performance prediction within limited prior knowledge of the unknown problem.

关键词： Fitness landscape analysis Random walk Differential evolution algorithm performance prediction Continuous optimization problem

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Data-based Approach to Predict Feasibility and Computational Requirement for Chemical Production Scheduling 13th

Data-based Approach to Predict Feasibility and Computational...

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13th IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems (DYCOPS)

作者： Kim, Boeun Maravelias, Christos T. Princeton Univ Andlinger Ctr Energy & Environm Princeton NJ 08544 USA Princeton Univ Dept Chem & Biol Engn Princeton NJ 08544 USA

Online scheduling requires frequent re-optimization to generate a schedule repeatedly accounting for updated information. However, if the time between re-optimizations is too short, then finding good, and in some cases even feasible, solutions can become challenging. This work proposed an approach, based on supervised learning techniques, to predict whether a given instance is feasible and, given that it is feasible, what is the computational requirement to solve the instance. Towards this goal, we introduce various types of features related to problem size, scheduling horizon, and processing times and costs that can be derived based on domain knowledge. Logistic regression and random forests models are trained as feasibility classifier and computational time regressor, respectively, using the dataset obtained from a wide variety of instances. Both show good predictive performances: F1 score similar to 0.90 and AUC similar to 0.98 for the feasibility classification and MSE similar to 0.5 for the computational time prediction. Finally, we discuss the features that are shown to be significant in the cases of makespan minimization and cost minimization. Copyright (C) 2022 The Authors.

关键词： Batch plant scheduling mixed integer programming supervised learning feasibility classification algorithm performance prediction

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Data-based Approach to Predict Feasibility and Computational Requirement for Chemical Production Scheduling

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IFAC-PapersOnLine 2022年第7期55卷 827-832页

作者： Boeun Kim Christos T. Maravelias Andlinger Center for Energy and the Environment Princeton University Princeton NJ 08544 USA Department of Chemical and Biological Engineering Princeton University Princeton NJ 08544 USA

Online scheduling requires frequent re-optimization to generate a schedule repeatedly accounting for updated information. However, if the time between re-optimizations is too short, then finding good, and in some cases even feasible, solutions can become challenging. This work proposed an approach, based on supervised learning techniques, to predict whether a given instance is feasible and, given that it is feasible, what is the computational requirement to solve the instance. Towards this goal, we introduce various types of features related to problem size, scheduling horizon, and processing times and costs that can be derived based on domain knowledge. Logistic regression and random forests models are trained as feasibility classifier and computational time regressor, respectively, using the dataset obtained from a wide variety of instances. Both show good predictive performances: F1 score ∼0.90 and AUC ∼0.98 for the feasibility classification and MSE ∼0.5 for the computational time prediction. Finally, we discuss the features that are shown to be significant in the cases of makespan minimization and cost minimization.

关键词： Batch plant scheduling mixed integer programming supervised learning feasibility classification algorithm performance prediction

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