Evolving Populations of Solved Subgraphs with Crossover and Constraint Repair [0.03%]
基于交叉和约束修正的解子图演化群体算法
Jiwon Lee,Mahya Salimi Gamasaei,Andrew M Sutton
Jiwon Lee
We introduce a population-based approach to solving parameterized graph problems for which the goal is to identify a small set of vertices subject to a feasibility criterion. The idea is to evolve a population of individuals where each indi...
EvolCAF: Automatic Cost-Aware Acquisition Function Design Using Large Language Models [0.03%]
基于大规模语言模型的自动成本感知获取函数设计（EvolCAF）
Yiming Yao,Fei Liu,Ji Cheng et al.
Yiming Yao et al.
To address optimization problems that involve expensive evaluations with unknown and heterogeneous costs, cost-aware Bayesian optimization (BO) emerges as a prominent solution in many real-world scenarios. However, as a critical step in dev...
Improving Performance of Algorithm Selection Pipelines on Large Instance Sets via Dynamic Reallocation of Budget [0.03%]
通过动态分配预算改善算法选择管道在大型实例数据上的性能
Quentin Renau,Emma Hart
Quentin Renau
Special Issue PPSN 2024: Algorithm-selection (AS) methods are essential in order to obtain the best performance from a portfolio of solvers. When considering large sets of instances that either arrive in a stream or in a single batch, there...
Double XCSF on Target? [0.03%]
双倍XCSF吗？
Connor Schönberner,Sven Tomforde
Connor Schönberner
The XCS Classifier System (XCS), the most prominent Learning Classifier System (LCS), originally focused on Reinforcement Learning (RL) problems. Over time, emphasis shifted heavily to supervised learning, with some applications in unsuperv...
Runtime Analysis of Evolutionary Diversity Optimization on the Multi-objective (LeadingOnes, TrailingZeros) Problem [0.03%]
演化多样性优化在多目标(leadingones,trailingzeros)问题上的运行时间分析
Denis Antipov,Aneta Neumann,Frank Neumann et al.
Denis Antipov et al.
Diversity optimization is the class of optimization problems in which we aim to find a diverse set of good solutions. One of the frequently-used approaches to solve such problems is to use evolutionary algorithms that evolve a desired diver...
Zhitong Ma,Jinghui Zhong
Zhitong Ma
Symbolic regression is a challenging task in machine learning that aims to automatically discover highly interpretable mathematical equations from limited data. Keen efforts have been devoted to addressing this issue, yielding promising res...
Fast Pareto Optimization Using Sliding Window Selection for Problems with Determinstic and Stochastic Constraints [0.03%]
基于滑动窗口选择的快速帕累托优化及其在确定性和随机约束问题中的应用
Frank Neumann,Carsten Witt
Frank Neumann
Submodular optimization problems play a key role in artificial intelligence as they allow to capture many important problems in machine learning, data science, and social networks. Pareto optimization using evolutionary multi-objective algo...
Exploring Automated Algorithm Design Synergizing Large Language Models and Evolutionary Algorithms: Survey and Insights [0.03%]
自动化算法设计综述与见解：大型语言模型和进化算法的协同作用探索
He Yu,Jing Liu
He Yu
Designing algorithms for optimization problems, no matter heuristic or meta-heuristic, often relies on manual design and domain expertise, limiting their scalability and adaptability. The integration of Large Language Models (LLMs) and Evol...
R2 v2: The Pareto-compliant R2 Indicator for Better Benchmarking in Bi-objective Optimization [0.03%]
改进的Pareto合规R2指标：二目标优化中更好的基准测试指标
Lennart Schäpermeier,Pascal Kerschke
Lennart Schäpermeier
In multi-objective optimization, set-based quality indicators are a cornerstone of benchmarking and performance assessment. They capture the quality of a set of tradeoff solutions by reducing it to a scalar number. One of the most commonly ...
All-Quadratic Mixed-Integer Problems: A Study on Evolution Strategies and Mathematical Programming [0.03%]
二次混合整数问题：进化策略与数学规划的研究
Guy Zepko,Ofer M Shir
Guy Zepko
Mixed-integer (MI) quadratic models subject to quadratic constraints, known as All- Quadratic MI Programs, constitute a challenging class of NP-complete optimization problems. The particular scenario of unbounded integers defines a subclass...