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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / International Joint Conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Friess, Stephen Tino, Peter Menzel, Stefan Xu, Zhao Sendhoff, Bernhard Yao, Xin Univ Birmingham Sch Comp Sci CERCIA Birmingham England NEC Labs Europe GmbH D-69115 Heidelberg Germany Southern Univ Sci & Technol Shenzhen Peoples R China

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

Traditional methods for solving problems within computer science rely mostly upon the application of handcrafted algorithms. As however manual engineering of them can be considered to be a tedious process, it is interesting to consider how far internal mechanisms can be directly learned in an end-to-end manner instead. This is especially tempting to consider for metaheuristic and evolutionary optimization routines which inherently rely upon creating abundant amounts of data during run-time. To implement such an approach for these types of algorithms, it effectively requires a pipeline to first acquire derandomized algorithm components in a domain-dependent manner and secondly a mapping to select them based upon characteristic features which unveil the black box character of an optimization problem. While in principle, within our prior work we proposed methods for extracting spatial features from metadata, these unfortunately fail to acknowledge the time-dependent nature of it. Thus, fail in scenarios when the inputs generated from initial iterations are not expressive enough. For this reason we specifically develop within this work architectures for spatio-temporal data processing. Particularly, we find that our proposed GCN-GRU and LSTM architectures, which take inspiration from CNN-LSTMs originally proposed for activity recognition in multimedia data-streams, demonstrate high efficiency and most consistent performance on time series of variable length. Further, we can also demonstrate that the class activation map (CAM) for interpretable learning with time series data helps to understand and reflects problem-dependent properties of the search behavior of an optimization algorithm.

关键词： Representation Learning algorithm selection Graph Neural Networks Activity Recognition Time Series Classification

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Self-Organising algorithms for Residential Demand Response 2

Self-Organising Algorithms for Residential Demand Response

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EEE Conference on Technologies for Sustainability (SusTech)

作者： Taylor, Adam Dusparic, Ivana Harris, Colin Marinescu, Andrei Galvan-Lopez, Edgar Golpayegani, Fatemeh Clarke, Siobhan Cahill, Vinny Trinity Coll Dublin Sch Comp Sci & Stat Distributed Syst Grp Dublin Ireland

ISBN: (纸本)9781479952380

Residential Demand Response has shown promising results in smart grid applications. It can be achieved manually or autonomously. The variety of algorithms applied to achieve autonomous Demand Response have lacked a common baseline. The selection of testing environment is typically skewed by the desire to find one in which a particular algorithm performs well. This work evaluates several algorithms in a common evaluation environment. Which has been designed to encompass the range of conditions in a typical Residential Demand Response application. The environments described exhibit the following characteristics: non-stationary, dynamic, multi-actor, multi-objective. These characteristics will then be used to provide heuristics for algorithm selection. The algorithms used were selected to cover the spectrum of possible approaches to Demand Response. Some are centralised, others distributed. There are collaborative approaches and non-collaborative ones. Some are learning based, others require no training. This work provides criteria for which particular algorithms should be applied to a given application.

关键词： demand side management power engineering computing self-organising feature maps algorithm selection collaborative approaches common evaluation environment residential demand response self-organising algorithms smart grid algorithm design and analysis Collaboration Heuristic algorithms Load management Prediction algorithms Schedules Water heating

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A Recommender System for Process Discovery

A Recommender System for Process Discovery

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12th International Conference on Business Process Management (BPM)

作者： Ribeiro, Joel Carmona, Josep Misir, Mustafa Sebag, Michele Univ Politecn Cataluna E-08028 Barcelona Spain Univ Paris Sud XI TAO INRIA Saclay CNRS LRI Orsay France

ISBN: (纸本)9783319101729;9783319101712

Over the last decade, several algorithms for process discovery and process conformance have been proposed. Still, it is well-accepted that there is no dominant algorithm in any of these two disciplines, and then it is often difficult to apply them successfully. Most of these algorithms need a close-to expert knowledge in order to be applied satisfactorily. In this paper, we present a recommender system that uses portfolio-based algorithm selection strategies to face the following problems: to find the best discovery algorithm for the data at hand, and to allow bridging the gap between general users and process mining algorithms. Experiments performed with the developed tool witness the usefulness of the approach for a variety of instances.

关键词： Process Mining Recommender Systems algorithm selection

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A Meta-Learning Method to Select Under-Sampling algorithms for Imbalanced Data Sets 5

A Meta-Learning Method to Select Under-Sampling Algorithms f...

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5th Brazilian Conference on Intelligent Systems (BRACIS)

作者： de Morais, Romero F. A. B. Miranda, Pericles B. C. Silva, Ricardo M. A. Univ Fed Pernambuco Recife PE Brazil

ISBN: (纸本)9781509035663

Imbalanced data sets originating from real world problems, such as medical diagnosis, can be found pervasive. Learning from imbalanced data sets poses its own challenges, as common classifiers assume a balanced distribution of examples' classes in the data. Sampling techniques overcome the imbalance in the data by modifying the examples' classes distribution. Unfortunately, selecting a sampling technique together with its parameters is still an open problem. Current solutions include the brute-force approach (try as many techniques as possible), and the random search approach (choose the most appropriate from a random subset of techniques). In this work, we propose a new method to select sampling techniques for imbalanced data sets. It uses Meta-Learning and works by recommending a technique for an imbalanced data set based on solutions to previous problems. Our experimentation compared the proposed method against the brute-force approach, all techniques with their default parameters, and the random search approach. The results of our experimentation show that the proposed method is comparable to the brute-force approach, outperforms the techniques with their default parameters most of the time, and always surpasses the random search approach.

关键词： Meta-learning algorithm selection Sampling algorithms

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Integrated vs. Sequential Approaches for Selecting and Tuning CMA-ES Variants

Integrated vs. Sequential Approaches for Selecting and Tunin...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Vermetten, Diederick Wang, Hao Doerr, Carola Back, Thomas Leiden Inst Adv Comp Sci Leiden Netherlands Sorbonne Univ CNRS LIP6 Paris France

ISBN: (纸本)9781450371285

When faced with a specific optimization problem, deciding which algorithm to apply is always a difficult task. Not only is there a vast variety of algorithms to select from, but these algorithms are often controlled by many hyperparameters, which need to be suitably tuned in order to achieve peak performance. Usually, the problem of selecting and configuring the optimization algorithm is addressed sequentially, by first selecting a suitable algorithm and then tuning it for the application at hand. Integrated approaches, commonly known as Combined algorithm selection and Hyperparameter (CASH) solvers, have shown promise in several applications. In this work we compare sequential and integrated approaches for selecting and tuning the best out of the 4,608 variants of the modular Covariance Matrix Adaptation Evolution Strategy (CMA-ES). We show that the ranking of these variants depends to a large extent on the quality of the hyperparameters. Sequential approaches are therefore likely to recommend sub-optimal choices. Integrated approaches, in contrast, manage to provide competitive results at much smaller computational cost. We also highlight important differences in the search behavior of two CASH approaches, which build on racing (irace) and on model-based optimization (MIP-EGO), respectively.

关键词： algorithm selection algorithm Control Iterative Optimization Heuristics Black-Box Optimization

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Learning to select branching rules in the DPLL procedure for satisfiability

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Electronic Notes in Discrete Mathematics 2001年 9卷 344-359页

作者： Lagoudakis, Michail G. Littman, Michael L. Department of Computer Science Duke University Durham NC 27708 United States Shannon Laboratory AT and T Labs. Research Florham Park NJ 07932 United States

The DPLL procedure is the most popular complete satisfiability (SAT) solver. While its worst case complexity is exponential, the actual running time is greatly affected by the ordering of branch variables during the search. Several branching rules have been proposed, but none is the best in all cases. This work investigates the use of automated methods for choosing the most appropriate branching rule at each node in the search tree. We consider a reinforcement-learning approach where a value function, which predicts the performance of each branching rule in each case, is learned through trial runs on a typical problem set of the target class of SAT problems. Our results indicate that, provided sufficient training on a given class, the resulting strategy performs as well as (and, in some cases, better than) the best branching rule for that class.

关键词： algorithm selection Branching Rules Reinforcement Learning Satisfiability Value Function Approximation

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Landscape Analysis Based vs. Domain-Specific Optimization for Engineering Design Applications: A Clear Case 2

Landscape Analysis Based vs. Domain-Specific Optimization fo...

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2nd IEEE Conference on Artificial Intelligence (CAI)

作者： de Winter, Roy Long, Fu Xing Thomaser, Andre Back, Thomas H. W. van Stein, Niki Kononova, Anna, V Leiden Univ LIACS Leiden Netherlands BMW Grp Munich Germany

ISBN: (纸本)9798350354102;9798350354096

Traditional methods in black-box optimization often prescribe general-purpose algorithms for broad problem categories, which overlooks the significant variability in optimization landscapes that can exist even within a single domain. We address this gap by demonstrating the efficacy of landscape analysis techniques in guiding the choice and tuning of optimization algorithms for distinct landscape features. This study focuses on two real-world problem domains characterized by blackbox simulation-based optimization: automotive control system calibration (anti-lock braking system) and automotive crashworthiness optimization. Through comparative analysis of computer experiments in these domains, landscape analysis techniques are employed for both visualization and explanation purposes. The results reveal substantial variations in landscape features across different instances within the same problem domain. Consequently, we advocate for a paradigm shift towards learning and applying optimizers tailored to specific landscape feature spaces rather than applying a one-size-fits-all approach to an entire problem domain. This research not only enhances the understanding of landscape variability in optimization problems but also paves the way for more efficient and effective optimization processes in complex, real-world scenarios.

关键词： Landscape Analysis algorithm selection Black Box Optimization Engineering Optimization

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Rigorous Performance Analysis of State-of-the-Art TSP Heuristic Solvers 1

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19th European Conference on Evolutionary Computation in Combinatorial Optimization (EvoCOP)

作者： McMenemy, Paul Veerapen, Nadarajen Adair, Jason Ochoa, Gabriela Univ Stirling Comp Sci & Math Stirling Scotland Univ Lille CNRS Cent Lille UMR 9189CRIStAL F-59000 Lille France

ISBN: (数字)9783030167110

ISBN: (纸本)9783030167103;9783030167110

Understanding why some problems are better solved by one algorithm rather than another is still an open problem, and the symmetric Travelling Salesperson Problem (TSP) is no exception. We apply three state-of-the-art heuristic solvers to a large set of TSP instances of varying structure and size, identifying which heuristics solve specific instances to optimality faster than others. The first two solvers considered are variants of the multi-trial Helsgaun's Lin-Kernighan Heuristic (a form of iterated local search), with each utilising a different form of Partition Crossover;the third solver is a genetic algorithm (GA) using Edge Assembly Crossover. Our results show that the GA with Edge Assembly Crossover is the best solver, shown to significantly outperform the other algorithms in 73% of the instances analysed. A comprehensive set of features for all instances is also extracted, and decision trees are used to identify main features which could best inform algorithm selection. The most prominent features identified a high proportion of instances where the GA with Edge Assembly Crossover performed significantly better when solving to optimality.

关键词： TSP algorithm selection EAX GPX Performance analysis

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Evolving Reliable Differentiating Constraints for the Chance-constrained Maximum Coverage Problem

Evolving Reliable Differentiating Constraints for the Chance...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Ahouei, Saba Sadeghi de Nobel, Jacob Neumann, Aneta Back, Thomas Neumann, Frank Univ Adelaide Sch Comp & Math Sci Optimisat & Logist Adelaide SA Australia Leiden Univ LIACS Leiden Netherlands

ISBN: (纸本)9798400704949

Chance-constrained problems involve stochastic components in the constraints which can be violated with a small probability. We investigate the impact of different types of chance constraints on the performance of iterative search algorithms and study the classical maximum coverage problem in graphs with chance constraints. Our goal is to evolve reliable chance constraint settings for a given graph where the performance of algorithms differs significantly not just in expectation but with high confidence. This allows to better learn and understand how different types of algorithms can deal with different types of constraint settings and supports automatic algorithm selection. We develop an evolutionary algorithm that provides sets of chance constraints that differentiate the performance of two stochastic search algorithms with high confidence. We initially use traditional approximation ratio as the fitness function of (1+1) EA to evolve instances, which shows inadequacy to generate reliable instances. To address this issue, we introduce a new measure to calculate the performance difference for two algorithms, which considers variances of performance ratios. Our experiments show that our approach is highly successful in solving the instability issue of the performance ratios and leads to evolving reliable sets of chance constraints with significantly different performance for various types of algorithms.

关键词： Evolutionary algorithms chance-constrained optimization benchmarking algorithm selection

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Comparison of Differential Evolution algorithms on the Mapping Between Problems and Penalty Parameters 8th

Comparison of Differential Evolution Algorithms on the Mappi...

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8th International Conference on Swarm Intelligence (ICSI)

作者： Si, Chengyong Shen, Jianqiang Zou, Xuan Wang, Lei Univ Shanghai Sci & Technol Shanghai Hamburg Coll Shanghai 200093 Peoples R China Tongji Univ Coll Elect & Informat Engn Shanghai 201804 Peoples R China

ISBN: (纸本)9783319618241;9783319618234

Penalty parameters play a key role when adopting the penalty function method for solution ranking. In the previous study, a corresponding relationship between the constrained optimization problems and the penalty parameters was constructed. This paper tries to verify whether the relationship is related with the evolutionary algorithms (EAs), i.e., how the EAs influence the relationship. Two differential evolution algorithms are taken as an example. Experimental results confirm the influence and show that an improved EA will enlarge the available value of corresponding penalty parameter, especially for the intermittent relationship. The findings also prove that EA can make up the shortcoming of constraint handling techniques to some extent.

关键词： Constrained optimization Constraint handling techniques Differential evolution Penalty parameter algorithm selection

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