P-NP instance decomposition based on the Fourier transform for solving the Linear Ordering Problem [0.03%]
基于傅里叶变换的P-NP实例分解在解决线性排序问题中的应用
Xabier Benavides,Leticia Hernando,Josu Ceberio et al.
Xabier Benavides et al.
The Fourier transform over finite groups has proved to be a useful tool for analyzing combinatorial optimization problems. However, few heuristic and meta-heuristic algorithms have been proposed in the literature that utilize the informatio...
Deep-ELA: Deep Exploratory Landscape Analysis with Self-Supervised Pretrained Transformers for Single- and Multi-Objective Continuous Optimization Problems [0.03%]
基于自监督预训练变换器的深度探索景观分析在单目标和多目标连续优化问题中的应用
Moritz Vinzent Seiler,Pascal Kerschke,Heike Trautmann
Moritz Vinzent Seiler
In many recent works,the potential of Exploratory Landscape Analysis (ELA) features to numerically characterize single-objective continuous optimization problems has been demonstrated. These numerical features provide the input for all kind...
Quality Diversity under Sparse Interaction and Sparse Reward: Application to Grasping in Robotics [0.03%]
基于稀疏交互和稀疏奖励的质量多样性及其在机器人抓取中的应用
Johann Huber,François Helenon,Miranda Coninx et al.
Johann Huber et al.
Quality-Diversity (QD) methods are algorithms that aim to generate a set of diverse and highperforming solutions to a given problem. Originally developed for evolutionary robotics, most QD studies are conducted on a limited set of domains'm...
The Cost of Randomness in Evolutionary Algorithms: Crossover Can Save Random Bits [0.03%]
随机性在进化算法中的代价：重组可以节省随机位
Carlo Kneissl,Dirk Sudholt
Carlo Kneissl
Evolutionary algorithms make countless random decisions during selection, mutation and crossover operations. These random decisions require a steady stream of random numbers. We analyze the expected number of random bits used throughout a r...
Survey of interactive evolutionary decomposition-based multiobjective optimization methods [0.03%]
交互式进化约简多目标优化方法综述
Giomara Lárraga,Kaisa Miettinen
Giomara Lárraga
Interactive methods support decision-makers in finding the most preferred solution for multiobjective optimization problems, where multiple conflicting objective functions must be optimized simultaneously. These methods let a decision-maker...
Runtime Analysis of Typical Decomposition Approaches in MOEA/D for Many-Objective Optimization Problems [0.03%]
MOEA/D求解多目标优化问题的典型分解方法的运行时间分析
Zhengxin Huang,Yunren Zhou,Zefeng Chen et al.
Zhengxin Huang et al.
Decomposition-based multi-objective evolutionary algorithms (MOEAs) are popular methods utilized to address many-objective optimization problems (MaOPs). These algorithms decompose the original MaOP into several scalar optimization subprobl...
Genetic Programming-based Feature Selection for Symbolic Regression on Incomplete Data [0.03%]
基于遗传规划的符号回归的不完备数据特征选择方法
Baligh Al-Helali,Qi Chen,Bing Xue et al.
Baligh Al-Helali et al.
High-dimensionality is one of the serious real-world data challenges in symbolic regression and it is more challenging if the data are incomplete. Genetic programming has been successfully utilised for high-dimensional tasks due to its natu...
Ryoki Hamano,Kento Uchida,Shinichi Shirakawa et al.
Ryoki Hamano et al.
The majority of theoretical analyses of evolutionary algorithms in the discrete domain focus on binary optimization algorithms, even though black-box optimization on the categorical domain has a lot of practical applications. In this paper,...
Optimizing Monotone Chance-Constrained Submodular Functions Using Evolutionary Multi-Objective Algorithms [0.03%]
利用进化多目标算法优化单调机会约束子模函数
Aneta Neumann,Frank Neumann
Aneta Neumann
Many real-world optimization problems can be stated in terms of submodular functions. Furthermore, these real-world problems often involve uncertainties which may lead to the violation of given constraints. A lot of evolutionary multi-objec...
Genetic Programming for Automatically Evolving Multiple Features to Classification [0.03%]
遗传编程自动演化分类的多个特征
Peng Wang,Bing Xue,Jing Liang et al.
Peng Wang et al.
Performing classification on high-dimensional data poses a significant challenge due to the huge search space. Moreover, complex feature interactions introduce an additional obstacle. The problems can be addressed by using feature selection...