Proceedings of the Companion Conference on Genetic and Evolutionary Computation最新文献_第6页

Genotype Diversity Measures for Escaping Plateau Regions in University Course Timetabling 逃避高原地区大学课程安排的基因型多样性措施

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3596334

James Sakal, J. Fieldsend, E. Keedwell

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Theoretical Limits on the Success of Lexicase Selection Under Contradictory Objectives 目标矛盾条件下词法选择成功的理论限制

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3590714

Shakiba Shahbandegan, Emily L. Dolson

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Performance Comparison of Steady State GAs and Generational GAs for Capacitated Vehicle Routing Problems 稳态气体与分代气体在车辆路径问题中的性能比较

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3590614

Jose Quevedo, Maxi Heer, Marwan F. Abdelatti, Resit Sendag, M. Sodhi

引用次数: 0

Evolutionary Algorithms for Segment Optimization in Vectorial GP 向量GP中分段优化的进化算法

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3590668

Philipp Fleck, Stephan M. Winkler, M. Kommenda, Sara Silva, L. Vanneschi, M. Affenzeller

{"title":"Evolutionary Algorithms for Segment Optimization in Vectorial GP","authors":"Philipp Fleck, Stephan M. Winkler, M. Kommenda, Sara Silva, L. Vanneschi, M. Affenzeller","doi":"10.1145/3583133.3590668","DOIUrl":"https://doi.org/10.1145/3583133.3590668","url":null,"abstract":"Vectorial Genetic Programming (Vec-GP) extends regular GP by allowing vectorial input features (e.g. time series data), while retaining the expressiveness and interpretability of regular GP. The availability of raw vectorial data during training, not only enables Vec-GP to select appropriate aggregation functions itself, but also allows Vec-GP to extract segments from vectors prior to aggregation (like windows for time series data). This is a critical factor in many machine learning applications, as vectors can be very long and only small segments may be relevant. However, allowing aggregation over segments within GP models makes the training more complicated. We explore the use of common evolutionary algorithms to help GP identify appropriate segments, which we analyze using a simplified problem that focuses on optimizing aggregation segments on fixed data. Since the studied algorithms are to be used in GP for local optimization (e.g. as mutation operator), we evaluate not only the quality of the solutions, but also take into account the convergence speed and anytime performance. Among the evaluated algorithms, CMA-ES, PSO and ALPS show the most promising results, which would be prime candidates for evaluation within GP.","PeriodicalId":422029,"journal":{"name":"Proceedings of the Companion Conference on Genetic and Evolutionary Computation","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115765511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Ant Colony Algorithm Assisted by Graph Neural Networks for Solving Vehicle Routing Problems 图神经网络辅助蚁群算法求解车辆路径问题

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3596424

Xiangyu Wang, Yaochu Jin

引用次数: 0

Tree-Based Grammatical Evolution with Non-Encoding Nodes 基于树的非编码节点语法演化

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3596944

Marina de la Cruz López, O. Garnica, J. Hidalgo

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Addressing the Health Versus Economy Dilemma in Data-Driven Policymaking During a Pandemic 解决大流行期间数据驱动决策中的健康与经济困境

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3590652

Lewis Hotchkiss, A. Rahat

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Landscape Analysis of Optimization Problems and Algorithms 景观分析的优化问题与算法

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3595051

K. Malan, G. Ochoa

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A comprehensive and brief survey on interactive evolutionary computation in sound and music composition for algorithmic auditory and acoustic design with human-in-the-loop 基于人在环的算法听觉和声学设计中声音和音乐创作的交互进化计算的综合简要综述

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3596301

Yan Pei

{"title":"A comprehensive and brief survey on interactive evolutionary computation in sound and music composition for algorithmic auditory and acoustic design with human-in-the-loop","authors":"Yan Pei","doi":"10.1145/3583133.3596301","DOIUrl":"https://doi.org/10.1145/3583133.3596301","url":null,"abstract":"We present a comprehensive survey on the topic of sound and music composition using interactive evolutionary computation (IEC). After explaining the fundamentals of IEC and computer music, we investigate the research subject from various aspects, including the presentation of sound and music in computer programming, IEC optimization algorithm studies, fitness function design, and interface design and evaluation methods in IEC. These subjects drive the theoretical study in this field. There are a variety of studies addressing IEC application-oriented studies, and we summarize the findings related to auditory and acoustic design with IEC. Knowledge discovery of auditory perception with IEC and IEC for auditory aid are two unique research subjects using IEC. Finally, we discuss potential research directions and subjects, including IEC for physiological and psychological research of auditory and the extension of IEC into the acoustic design of long-time music genres. IEC is not only a technique for optimization in auditory and acoustic design with human factors but also an implementation tool to achieve intelligent composition systems and creative art design with human-in-the-loop. It is a powerful tool to implement an artificial intelligence system with more considerations of human factors.","PeriodicalId":422029,"journal":{"name":"Proceedings of the Companion Conference on Genetic and Evolutionary Computation","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125276430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Explaining a Staff Rostering Genetic Algorithm using Sensitivity Analysis and Trajectory Analysis. 用灵敏度分析和轨迹分析解释员工名册遗传算法。

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3596353

Martin Fyvie, J. Mccall, Lee A. Christie, A. Brownlee

{"title":"Explaining a Staff Rostering Genetic Algorithm using Sensitivity Analysis and Trajectory Analysis.","authors":"Martin Fyvie, J. Mccall, Lee A. Christie, A. Brownlee","doi":"10.1145/3583133.3596353","DOIUrl":"https://doi.org/10.1145/3583133.3596353","url":null,"abstract":"In the field of Explainable AI, population-based search metaheuristics are of growing interest as they become more widely used in critical applications. The ability to relate key information regarding algorithm behaviour and drivers of solution quality to an end-user is vital. This paper investigates a novel method of explanatory feature extraction based on analysis of the search trajectory and compares the results to those of sensitivity analysis using \"Weighted Ranked Biased Overlap\". We apply these techniques to search trajectories generated by a genetic algorithm as it solves a staff rostering problem. We show that there is a significant overlap between these two explainability methods when identifying subsets of rostered workers whose allocations are responsible for large portions of fitness change in an optimization run. Both methods identify similar patterns in sensitivity, but our method also draws out additional information. As the search progresses, the techniques reveal how individual workers increase or decrease in the influence on the overall rostering solution's quality. Our method also helps identify workers with a lower impact on overall solution fitness and at what stage in the search these individuals can be considered highly flexible in their roster assignment.","PeriodicalId":422029,"journal":{"name":"Proceedings of the Companion Conference on Genetic and Evolutionary Computation","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122627341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0