Designing/configuring ensemble Differential Evolution (DE) algorithms with complementary search characteristics is a complex problem requiring both in-depth understanding of the constituent algorithm's dynamics an...
详细信息
ISBN:
(纸本)9783031451690;9783031451706
Designing/configuring ensemble Differential Evolution (DE) algorithms with complementary search characteristics is a complex problem requiring both in-depth understanding of the constituent algorithm's dynamics and tacit knowledge. This paper proposes a Grammatical Evolution (GE) based automatedconfiguration of a recent ensemble DE algorithm - Improved Multi-population Ensemble Differential Evolution (IMPEDE). A Backus Naur Form grammar, with nine ensemble and DE parameters, has been designed to represent all possible IMPEDE configurations. The proposed approach has been employed to evolve IMPEDE configurations that solve CEC'17 benchmark optimization problems. The evolved configurations have been validated on CEC'14 suite and a real-world optimization problem - economic load dispatch (ELD) problem from CEC'11 suite. The simulation experiments demonstrate that the proposed approach is capable of evolving IMPEDE configurations that exhibit statistically superior or comparable performance against the manual configuration of IMPEDE as well as against other prominent ensemble DE algorithms.
With the goal of devising algorithms for decision support in operational tasks, we introduce a new methodology for the automatedconfiguration of algorithms for combinatorial online optimization problems. The procedur...
详细信息
With the goal of devising algorithms for decision support in operational tasks, we introduce a new methodology for the automatedconfiguration of algorithms for combinatorial online optimization problems. The procedure draws upon available instance data and is capable of recognizing data patterns which prove beneficial to the overall outcome. Since online optimization requires repetitive decision making without complete future information, no online algorithm can be optimal for every instance and it is reasonable to restrict attention to rule-based algorithms. We consider such algorithms in the form of procedures which derive their decisions using a threshold value. Threshold values are computed by evaluating a mathematical term (threshold value expression) composed of the available instance data elements. The goal then consists of determining the structure of the threshold value expression leading to the best algorithm performance. To this end, we employ a simulated annealing scheme returning the most favorable term composition given the available instance data. The resulting methodology can be implemented as part of data-driven decision support systems in order to facilitate knowledge-based decision making. Decision rules are generated in an automated fashion once historical input data is provided. The methodology is successfully instantiated in a series of computational experiments for three classes of combinatorial online optimization problems (scheduling, packing, lot sizing). Results show that automatically configured online algorithms are even capable of substantially outperforming well-known online algorithms in respective problem settings. We attribute this effect to the methodology's capability of integrating instance data into the process of algorithmconfiguration.
Despite their great success in recent years, neural networks have been found to be vulnerable to adversarial attacks. These attacks are often based on slight perturbations of given inputs that cause them to be misclas...
详细信息
Despite their great success in recent years, neural networks have been found to be vulnerable to adversarial attacks. These attacks are often based on slight perturbations of given inputs that cause them to be misclassified. Several methods have been proposed to formally prove robustness of a given network against such attacks. However, these methods typically give rise to high computational demands, which severely limit their scalability. Recent state-of-the-art approaches state the verification task as a minimisation problem, which is formulated and solved as a mixed-integer linear programming (MIP) problem. We extend this approach by leveraging automated algorithm configuration techniques and, more specifically, construct a portfolio of MIP solver configurations optimised for the neural network verification task. We test this approach on two recent, state-of-the-art MIP-based verification engines, MIPVerify and Venus, and achieve substantial improvements in CPU time by average factors of up to 4.7 and 10.3, respectively.
Automatic algorithmconfiguration procedures aim at supporting the design and application of optimization algorithms by providing specialized tools to automatically adjust their parameters to use the available computa...
详细信息
ISBN:
(纸本)9798400701207
Automatic algorithmconfiguration procedures aim at supporting the design and application of optimization algorithms by providing specialized tools to automatically adjust their parameters to use the available computational resources effectively. The irace configurator is a state-of-the-art method implementing an iterated racing procedure that, in its current implementation, allows parallel executions of the target algorithm. Parallel evaluation is crucial for the efficient use of computational resources, reducing the wall-clock time that the configuration process needs to find a good configuration. In this work, we propose an alternative to irace that performs a single race instead of iterative races. The continuously racing configurator (crace) evaluates, removes and generates new configurations asynchronously, granting a high level of flexibility regarding the configuration process when compared to the previous iterative scheme. In this paper, we provide a general description of the crace configuration procedure and perform initial exploratory experiments on five configuration scenarios. These experiments focus on evaluating the effect of the minimum number of configurations required to be involved in the race and the number of parallel evaluations. The first results are encouraging, showing that crace can be a competitive procedure on the scenarios evaluated.
This paper summarizes our work "automatedconfiguration of Genetic algorithms by Tuning for Anytime Performance", to appear in IEEE Transactions on Evolutionary Computation. Finding the best configuration of...
详细信息
ISBN:
(纸本)9781450392686
This paper summarizes our work "automatedconfiguration of Genetic algorithms by Tuning for Anytime Performance", to appear in IEEE Transactions on Evolutionary Computation. Finding the best configuration of algorithms' hyperparameters for a given optimization problem is an important task in evolutionary computation. We compare in our work the results of four different hyperparameter optimization approaches for a family of genetic algorithms on 25 diverse pseudo-Boolean optimization problems. More precisely, we compare previously obtained results from a grid search with those obtained from three automatedconfiguration techniques: iterated racing, mixed-integer parallel efficient global optimization, and mixed-integer evolutionary strategies. Using two different cost metrics, expected running time and the area under the empirical cumulative distribution function curve, we find that in several cases the best configurations with respect to expected running time are obtained when using the area under the empirical cumulative distribution function curve as the cost metric during the configuration process. Our results suggest that even when interested in expected running time performance, it might be preferable to use anytime performance measures for the configuration task. We also observe that tuning for expected running time is much more sensitive with respect to the budget that is allocated to the target algorithms.
Nowadays software is often highly configurable, and the required adaptation is a complex and tedious task when performed manually. Moreover, hand-crafted configurations are often far from optimal. In this paper, we st...
详细信息
ISBN:
(纸本)9781728141367
Nowadays software is often highly configurable, and the required adaptation is a complex and tedious task when performed manually. Moreover, hand-crafted configurations are often far from optimal. In this paper, we study the software configuration problem in the context of the model comparison tool SiDiff, which needs to be carefully adapted to domain-specific modeling languages used in model-driven engineering. To tackle the configuration challenge, we propose to draw from the field of automated algorithm configuration, a research area which has studied the optimization of parameterizable algorithms for many years and which has gained particular momentum through its applications to hyper-parameter tuning in machine learning. Specifically, we report on ongoing work encoding the adaptability of SiDiff as an algorithmconfiguration problem which is amenable to a sequential model-based optimization tool known as SMAC. While empirical evaluation results are left for future work, the main goal of this paper is to foster active discussions at the workshop and to collect early feedback on our ongoing research.
Clustering is an important technique in data analysis which can reveal hidden patterns and unknown relationships in the data. A common problem in clustering is the proper choice of parameter settings. To tackle this, ...
详细信息
ISBN:
(纸本)9783030438234;9783030438227
Clustering is an important technique in data analysis which can reveal hidden patterns and unknown relationships in the data. A common problem in clustering is the proper choice of parameter settings. To tackle this, automated algorithm configuration is available which can automatically find the best parameter settings. In practice, however, many of our today's data sources are data streams due to the widespread deployment of sensors, the internet-of-things or (social) media. Stream clustering aims to tackle this challenge by identifying, tracking and updating clusters over time. Unfortunately, none of the existing approaches for automated algorithm configuration are directly applicable to the streaming scenario. In this paper, we explore the possibility of automated algorithm configuration for stream clustering algorithms using an ensemble of different configurations. In first experiments, we demonstrate that our approach is able to automatically find superior configurations and refine them over time.
Metaheuristic design is an incremental and difficult task. It is usually iterative and requires several evaluations of the code to obtain an algorithm with good performance. In this work, we analyse the design of meta...
详细信息
ISBN:
(纸本)9783319107622;9783319107615
Metaheuristic design is an incremental and difficult task. It is usually iterative and requires several evaluations of the code to obtain an algorithm with good performance. In this work, we analyse the design of metaheuristics by detecting components which are strictly necessary to obtain a good performance (in term of solutions quality). We use a collective strategy where the information generated by a tuner is used to detect the components usefulness. We evaluate this strategy with two well-known tuners EVOCA and I-RACE to analyse which one is more suitable and provides better results to make this components detection. The goal is to help the designer either to evaluate during the design process different options of the code or to simplify her/his final code without a loss in the quality of the solutions.
Most optimization problems of practical significance are typically solved by highly configurable parameterized *** achieve the best performance on a problem instance,a trial-and-error configuration process is required...
详细信息
Most optimization problems of practical significance are typically solved by highly configurable parameterized *** achieve the best performance on a problem instance,a trial-and-error configuration process is required,which is very costly and even prohibitive for problems that are already computationally intensive,*** problems associated with machine learning *** the past decades,many studies have been conducted to accelerate the tedious configuration process by learning from a set of training *** article refers to these studies as learn to optimize and reviews the progress achieved.
Developers of high-performance algorithms for hard computational problems increasingly take advantage of automated parameter tuning and algorithmconfiguration tools, and consequently often create solvers with many pa...
详细信息
Developers of high-performance algorithms for hard computational problems increasingly take advantage of automated parameter tuning and algorithmconfiguration tools, and consequently often create solvers with many parameters and vast configuration spaces. However, there has been very little work to help these algorithm developers answer questions about the high-quality configurations produced by these tools, specifically about which parameter changes contribute most to improved performance. In this work, we present an automated technique for answering such questions by performing ablation analysis between two algorithmconfigurations. We perform an extensive empirical analysis of our technique on five scenarios from propositional satisfiability, mixed-integer programming and AI planning, and show that in all of these scenarios more than 95 % of the performance gains between default configurations and optimised configurations obtained from automatedconfiguration tools can be explained by modifying the values of a small number of parameters (1-4 in the scenarios we studied). We also investigate the use of our ablation analysis procedure for producing configurations that generalise well to previously-unseen problem domains, as well as for analysing the structure of the algorithm parameter response surface near and between high-performance configurations.
暂无评论