Computing Optimal Populations for Binary Problems using Logic Minimization [0.03%]
基于逻辑最优化的二进制问题最优解计算方法研究
Pier Luca Lanzi
Pier Luca Lanzi
The study of generalization in XCS has been mainly focused on single-step binary problems for which the optimal (accurate, maximally general) solution is known. In contrast, binary multi-step problems have primarily been studied in terms of...
Enhancing Generalization and Scalability for Multi-Objective Optimization with Population Pre-Training [0.03%]
基于种群预训练的多目标优化泛化性和扩展性增强方法
Haokai Hong,Liang Feng,Min Jiang et al.
Haokai Hong et al.
Multi-objective optimization problems (MOPs) require the simultaneous optimization of conflicting objectives. Real-world MOPs often exhibit complex characteristics, including high-dimensional decision spaces, many objectives, or computation...
Fumito Uwano,Will N Browne
Fumito Uwano
Sequential perceptual aliasing is a cognitive challenge for learning agents when robots cannot differentiate states and their associations based on immediate observations, leading to poor decision-making. Existing systems struggle to abstra...
A dynamic multi-objective evolutionary algorithm using dual-space prediction and surrogate-based sampling [0.03%]
基于预测和代理样本的双空间动态多目标进化算法
Tianyu Liu,Xiangfei Wu,He Xu
Tianyu Liu
The main challenge in handling dynamic multi-objective optimization problems lies in the need for algorithms to accurately track Pareto-optimal solutions in constantly changing environments. Most existing predictionbased dynamic multi-objec...
Adapting MOEA/D to CMA-ES for Dealing with Ill-conditioned Multiobjective Problems [0.03%]
采用CMA-ES适应MOEA/D以处理病态多目标问题
Chengyu Lu,Zhenhua Li,Qingfu Zhang
Chengyu Lu
Ill-conditioned problems are widely acknowledged as a major challenge in singleobjective optimization, yet they remain largely unexplored in evolutionary multiobjective optimization. In this paper, we introduce a decomposition-based multiob...
Editorial of the Special Issue: Parallel Problem Solving from Nature PPSN 2024 Extended Versions of Best Paper Candidates [0.03%]
特刊编辑部：2024年平行问题求解自然大会(PPSN)最佳论文扩展版
M Affenzeller,S M Winkler,A V Kononova et al.
M Affenzeller et al.
Adaptive Sampled Walk: A Simple and Efficient Autonomous Local Search [0.03%]
自适应抽样行走：一种简单有效的自主局部搜索方法
Matthieu Basseur,Arnaud Liefooghe,Sara Tari
Matthieu Basseur
We introduce and explore the automation and adaptation of partial neighborhood local search. Unlike traditional approaches requiring extensive parameter tuning, we design our approach to operate with minimal prerequisites. Specifically, we ...
Improving CMA-ES convergence speed, efficiency, and reliability in noisy robot optimization problems [0.03%]
基于CMA-ES的噪声环境下机器人优化方法研究
Russell M Martin,Steven H Collins
Russell M Martin
Experimental robot optimization often requires evaluating each candidate policy for seconds to minutes. The chosen evaluation time influences optimization because of a speed-accuracy tradeoff: shorter evaluations enable faster iteration, bu...
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...