Suchergebnisse - "Automatic Algorithm Design"

Automatic Algorithm Design for Hybrid Flowshop Scheduling Problems von Alfaro-Fernández, Pedro, Ruiz, Rubén, Pagnozzi, Federico, Stützle, Thomas

“… •We present an Automatic Algorithm Design system for the hybrid flowshop problem …”
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Evaluating the impact of grammar complexity in automatic algorithm design von Pagnozzi, Federico, Stützle, Thomas

“… Grammar‐based automatic algorithm design has been shown to generate stochastic local search algorithms that compete with or outperform state‐of‐the‐art methods …”
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Automatic algorithm design of distributed hybrid flowshop scheduling with consistent sublots von Zhang, Biao, Lu, Chao, Meng, Lei-lei, Han, Yu-yan, Hu, Jiang, Jiang, Xu-chu

“… The present-day globalized economy and diverse market demands have compelled an increasing number of manufacturing enterprises to move toward the distributed …”
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PSO-X: A Component-Based Framework for the Automatic Design of Particle Swarm Optimization Algorithms von Camacho-Villalon, Christian L., Dorigo, Marco, Stutzle, Thomas

“… The particle swarm optimization (PSO) algorithm has been the object of many studies and modifications for more than 25 years. Ranging from small refinements to …”
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Automatic design of constructive heuristics for a reconfigurable distributed flowshop group scheduling problem von Zhang, Biao, Meng, Lei-lei, Lu, Chao, Han, Yu-yan, Sang, Hong-yan

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Revisiting simulated annealing: A component-based analysis von Franzin, Alberto, Stützle, Thomas

“… •We show how to collect and classify variants of Simulated Annealing (SA) algorithms.•We use automatic configuration to improve existing Simulated Annealing …”
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Autonomous Multi-Objective Optimization Using Large Language Model von Huang, Yuxiao, Wu, Shenghao, Zhang, Wenjie, Wu, Jibin, Feng, Liang, Tan, Kay Chen

“… Multi-objective optimization problems (MOPs) are ubiquitous in real-world applications, presenting a complex challenge of balancing multiple conflicting …”
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An automatic multi-objective evolutionary algorithm for the hybrid flowshop scheduling problem with consistent sublots von Zhang, Biao, Pan, Quan-ke, Meng, Lei-lei, Lu, Chao, Mou, Jian-hui, Li, Jun-qing

“… Lot streaming is the most widely used technique to facilitate overlapping of successive operations. Inspired by real-world scenarios, this paper studies a …”
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Automatic design of hybrid stochastic local search algorithms for permutation flowshop problems von Pagnozzi, Federico, Stützle, Thomas

“… •We present a system for the automatic design of Hybrid Stochastic Local Searches.•Algorithms are obtained by combining components following rules defined by a …”
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Towards fast approximations for the hypervolume indicator for multi-objective optimization problems by Genetic Programming von Sandoval, Cristian, Cuate, Oliver, González, Luis C., Trujillo, Leonardo, Schütze, Oliver

“… Hypervolume (HV) has become one of the most popular indicators to assess the quality of Pareto front approximations. However, the best algorithm for computing …”
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LLM-Assisted Automatic Memetic Algorithm for Lot-Streaming Hybrid Job Shop Scheduling With Variable Sublots von Li, Rui, Wang, Ling, Sang, Hongyan, Yao, Lizhong, Pan, Lijun

“… This study addresses the lot-streaming hybrid job shop scheduling problem with variable sublots (LHJSV), inspired by a real-world aircraft tooling shop. A …”
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Evolving Generalizable Parallel Algorithm Portfolios for Binary Optimization Problems via Domain-Agnostic Instance Generation von Wang, Zhiyuan, Liu, Shengcai, Yang, Peng, Tang, Ke

“… Generalization is the core objective when training optimizers from data. However, limited training instances often constrain the generalization capability of …”
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A Large Language Model-based Multi-Agent Framework to Autonomously Design Algorithms for Earth Observation Satellite Scheduling Problem von Chen, Jiawei, Chen, Yingguo, Pham, Duc Trong, Song, Yanjie, Wu, Jian, Xing, Lining, Chen, Yingwu

“… The Earth observation satellite scheduling problem (EOSSP) is a complex optimization problem involving the selection, sequencing, and timing of observational …”
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Meta-Black-Box optimization for evolutionary algorithms: Review and perspective von Yang, Xu, Wang, Rui, Li, Kaiwen, Ishibuchi, Hisao

“… Originating from Automatic Algorithm Design (AAD), MetaBBO has branched into areas such as Learn to Optimize (L2O …”
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Real-time data-driven automatic design of multi-objective evolutionary algorithm: A case study on production scheduling von Zhang, Biao, Meng, Lei-lei, Lu, Chao, Li, Jun-qing

“… Multi-objective evolutionary algorithms (MOEAs) have become an important choice for solving multi-objective optimization problems. The performance of MOEAs is …”
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Exploratory landscape analysis on black-box optimization problems via Graph Neural Network von Yang, Xu, Wang, Rui, Li, Kaiwen, Li, Wenhua, Zhang, Tao

“… Most real-world optimization problems are poorly understood, some of which are black-box optimization problems (BBOPs). Exploratory landscape analysis (ELA) …”
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Automatic Algorithm Design Assisted by LLMs for Solving Vehicle Routing Problems von Ma, Liang, Hao, Xingxing, Yang, Ruoxi, Hai, Zhilan

“… Currently, automatic algorithm design for solving combinatorial optimization problems (COPs …”
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Incorporating decision-maker’s preferences into the automatic configuration of bi-objective optimisation algorithms von Diaz, Juan Esteban, López-Ibáñez, Manuel

“… •A visual way of eliciting decision-maker's (DM's) preferences is described.•Empirical attainment differences are mapped into a weighted hypervolume …”
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Automatically Designing State-of-the-Art Multi- and Many-Objective Evolutionary Algorithms von Bezerra, Leonardo C T, López-Ibáñez, Manuel, Stützle, Thomas

“… A recent comparison of well-established multiobjective evolutionary algorithms (MOEAs) has helped better identify the current state-of-the-art by considering …”
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Evolutionary Design of Decision-Tree Algorithms Tailored to Microarray Gene Expression Data Sets von Barros, Rodrigo C., Basgalupp, Marcio P., Freitas, Alex A., de Carvalho, Andre C. P. L. F.

“… Decision-tree induction algorithms are widely used in machine learning applications in which the goal is to extract knowledge from data and present it in a …”
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