Search Results - "Automated algorithm configuration"

Automated Algorithm Selection: Survey and Perspectives by Kerschke, Pascal, Hoos, Holger H, Neumann, Frank, Trautmann, Heike

“…It has long been observed that for practically any computational problem that has been intensely studied, different instances are best solved using different…”
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Learn to optimize—a brief overview by Tang, Ke, Yao, Xin

“…Most optimization problems of practical significance are typically solved by highly configurable parameterized algorithms. To achieve the best performance on a…”
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Speeding up neural network robustness verification via algorithm configuration and an optimised mixed integer linear programming solver portfolio by König, Matthias, Hoos, Holger H., Rijn, Jan N. van

“…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…”
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Analysing differences between algorithm configurations through ablation by Fawcett, Chris, Hoos, Holger H.

“…Developers of high-performance algorithms for hard computational problems increasingly take advantage of automated parameter tuning and algorithm configuration…”
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Automated Configuration of Evolutionary Algorithms via Deep Reinforcement Learning for Constrained Multiobjective Optimization by Ming, Fei, Gong, Wenyin, Xue, Bing, Zhang, Mengjie, Jin, Yaochu

“…Learning to optimize and automated algorithm design are attracting increasing attention, but it is still in its infancy in constrained multiobjective…”
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Exact stochastic constraint optimisation with applications in network analysis by Latour, Anna L.D., Babaki, Behrouz, Fokkinga, Daniël, Anastacio, Marie, Hoos, Holger H., Nijssen, Siegfried

“…We present an extensive study of methods for exactly solving stochastic constraint (optimisation) problems (SCPs) in network analysis. These problems are…”
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A data-driven methodology for the automated configuration of online algorithms by Dunke, Fabian, Nickel, Stefan

“…With the goal of devising algorithms for decision support in operational tasks, we introduce a new methodology for the automated configuration of algorithms…”
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Improving the computational efficiency of stochastic programs using automated algorithm configuration: an application to decentralized energy systems by Schwarz, Hannes, Kotthoff, Lars, Hoos, Holger, Fichtner, Wolf, Bertsch, Valentin

“…The optimization of decentralized energy systems is an important practical problem that can be modeled using stochastic programs and solved via their…”
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Continuous optimization algorithms for tuning real and integer parameters of swarm intelligence algorithms by Yuan, Zhi, Montes de Oca, Marco A., Birattari, Mauro, Stützle, Thomas

“…The performance of optimization algorithms, including those based on swarm intelligence, depends on the values assigned to their parameters. To obtain high…”
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Towards Feature-Free Automated Algorithm Selection for Single-Objective Continuous Black-Box Optimization by Prager, Raphael Patrick, Vinzent Seiler, Moritz, Trautmann, Heike, Kerschke, Pascal

“…We propose a novel method for automated algorithm selection in the domain of single-objective continuous black-box optimization. In contrast to existing…”
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Encoding Adaptability of Software Engineering Tools as Algorithm Configuration Problem: A Case Study by Basmer, Maike, Kehrer, Timo

“…Nowadays software is often highly configurable, and the required adaptation is a complex and tedious task when performed manually. Moreover, hand-crafted…”
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Automatic parameter configuration for an elite solution hyper-heuristic applied to the Multidimensional Knapsack Problem by Urra, Enrique, Cubillos, Claudio, Cabrera-Paniagua, Daniel, Lefranc, Gaston

“…Hyper-heuristics are methods for problem solving that decouple the search mechanisms from the domain features, providing a reusable approach across different…”
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An algorithm development environment for problem-solving: software review by Chen, Xianshun

“…Algorithm Development Environment for Problem Solving (ADEP) is a development platform catering to the needs of designing and exploring computationally viable…”
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