发布求助
文献互助 智能选刊 最新文献

Proceedings of the Companion Conference on Genetic and Evolutionary Computation最新文献

筛选
英文 中文
Investigating the Viability of Existing Exploratory Landscape Analysis Features for Mixed-Integer Problems 探讨混合整数问题现有探索性景观分析特征的可行性
Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3590757
Raphael Patrick Prager, H. Trautmann
{"title":"Investigating the Viability of Existing Exploratory Landscape Analysis Features for Mixed-Integer Problems","authors":"Raphael Patrick Prager, H. Trautmann","doi":"10.1145/3583133.3590757","DOIUrl":"https://doi.org/10.1145/3583133.3590757","url":null,"abstract":"Exploratory landscape analysis has been at the forefront of characterizing single-objective continuous optimization problems. Other variants, which can be summarized under the term landscape analysis, have been used in the domain of combinatorial problems. However, none to little has been done in this research area for mixed-integer problems. In this work, we evaluate the current state of existing exploratory landscape analysis features and their applicability on a subset of mixed-integer problems.","PeriodicalId":422029,"journal":{"name":"Proceedings of the Companion Conference on Genetic and Evolutionary Computation","volume":"80 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":"128740817","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
Efficient Vehicle Routing Problem: A Machine Learning and Evolutionary Computation Approach 高效车辆路线问题:一种机器学习和进化计算方法
Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3596425
Pratyay Mukherjee, Ramanathan A, S. Dey
{"title":"Efficient Vehicle Routing Problem: A Machine Learning and Evolutionary Computation Approach","authors":"Pratyay Mukherjee, Ramanathan A, S. Dey","doi":"10.1145/3583133.3596425","DOIUrl":"https://doi.org/10.1145/3583133.3596425","url":null,"abstract":"The Vehicle Routing Problem with Time Windows (VRPTW) is an extension of VRP that introduces time window constraints to the routing optimization process. Scaling Evolutionary Computation algorithms for VRPTW to handle large-scale problems poses significant challenges. Machine Learning assisted Evolutionary Computation strategy have been proposed to enhance optimization algorithms' efficiency and effectiveness. This study proposes a machine-learning model that exploits the graphical nature of VRP to design and improve evolutionary computational methods. The aim is to improve the resilience and efficiency of VRPTW optimization and provide better-quality solutions for practical applications.","PeriodicalId":422029,"journal":{"name":"Proceedings of the Companion Conference on Genetic and Evolutionary Computation","volume":"23 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":"128583798","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 Memristive Reservoir Computing Through Evolving Feature Attribution 从演化特征属性解释记忆储层计算
Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3590619
Xinming Shi, Zilu Wang, Leandro L. Minku, Xin Yao
{"title":"Explaining Memristive Reservoir Computing Through Evolving Feature Attribution","authors":"Xinming Shi, Zilu Wang, Leandro L. Minku, Xin Yao","doi":"10.1145/3583133.3590619","DOIUrl":"https://doi.org/10.1145/3583133.3590619","url":null,"abstract":"Memristive Reservoir Computing (MRC) is a promising computing architecture for time series tasks, but lacks explainability, leading to unreliable predictions. To address this issue, we propose an evolutionary framework to explain the time series predictions of MRC systems. Our proposed approach attributes the feature importance of the time series via an evolutionary approach to explain the predictions. Our experiments show that our approach successfully identified the most influential factors, demonstrating the effectiveness of our design and its superiority in terms of explanation compared to state-of-the-art methods.","PeriodicalId":422029,"journal":{"name":"Proceedings of the Companion Conference on Genetic and Evolutionary Computation","volume":"1 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":"129815448","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
Automated Algorithm Configuration and Design 自动化算法配置与设计
Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3595046
Leslie Pérez Cáceres, Manuel López-Ibáñez, T. Stützle
{"title":"Automated Algorithm Configuration and Design","authors":"Leslie Pérez Cáceres, Manuel López-Ibáñez, T. Stützle","doi":"10.1145/3583133.3595046","DOIUrl":"https://doi.org/10.1145/3583133.3595046","url":null,"abstract":"ACM 979-8-4007-0120-7/23/07. . . $15.00 https://doi.org/10.1145/3583133.3595046 Leslie Pérez Cáceres is an associated professor at Pontificia Universidad Católica de Valparáıso, Chile since 2018. She is also the Director of the Artificial Intelligence Diploma of the PUCV’s Escuela the Ingenieŕıa Informática. She received the M.S. degree in Engineering Sciences in 2011 from the Universidad Técnica Federico Santa Maŕıa, Valparáıso, Chile and the Ph.D. degree from the Université Libre de Bruxelles in 2017.","PeriodicalId":422029,"journal":{"name":"Proceedings of the Companion Conference on Genetic and Evolutionary Computation","volume":"10 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":"129831579","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
Genetic Programming for Automatic Design of Parameter Adaptation in Dual-Population Differential Evolution 基于遗传规划的双种群差异进化参数自适应自动设计
Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3596310
V. Stanovov, E. Semenkin
{"title":"Genetic Programming for Automatic Design of Parameter Adaptation in Dual-Population Differential Evolution","authors":"V. Stanovov, E. Semenkin","doi":"10.1145/3583133.3596310","DOIUrl":"https://doi.org/10.1145/3583133.3596310","url":null,"abstract":"The parameter adaptation is one of the main problems in many evolutionary algorithms, including differential evolution. Instead of manual development of new methods, a hyper-heuristic approach can be used, where an algorithm is applied to search for parameter adaptation scheme. In this study the symbolic regression genetic programming is applied to design parameter adaptation method for differential evolution algorithm with two populations L-NTADE. Due to algorithmic scheme different from popular L-SHADE, the L-NTADE may require specific adaptation mechanisms. Each solution in genetic programming consists of three trees, which generate scaling factor values based on current resource, success rate and current values in the memory cells, containing scaling factor and crossover rate. The training is performed on a set of 30 benchmark functions from CEC 2017 competition on numerical optimization, and at every generation of genetic programming new problem dimension, computational resource, optima location and rotation matrices are generated for every test function. The testing is performed on two benchmarks, CEC 2017 and CEC/GECCO 2022. The results comparison shows that the automatically designed parameter adaptation heuristics are capable of outperforming the success-history adaptation in many cases, including high-dimensional problems and problems with different computational resource.","PeriodicalId":422029,"journal":{"name":"Proceedings of the Companion Conference on Genetic and Evolutionary Computation","volume":"131 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":"130571322","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
Capacitated School Bus Routing Problem with Time Windows, Heterogeneous Fleets and Travel Assistants 具有时间窗、异构车队和出行助手的有能力校车路线问题
Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3596375
Ozioma Paul, J. Handl, Manuel López-Ibáñez
{"title":"Capacitated School Bus Routing Problem with Time Windows, Heterogeneous Fleets and Travel Assistants","authors":"Ozioma Paul, J. Handl, Manuel López-Ibáñez","doi":"10.1145/3583133.3596375","DOIUrl":"https://doi.org/10.1145/3583133.3596375","url":null,"abstract":"The School Bus Routing Problem (SBRP) is a well-known example of an optimization problem in which, given a variety of feasible solutions to the problem of transporting pupils to school, we seek out an optimal solution. With over 1.3 million students in England having special education needs and a growing demand for inclusion in the educational community, creating optimal and inclusive school bus routes is essential to promoting this inclusion. The standard SBRP is a highly constrained problem. When considering the needs of pupils with Special Educational Needs and Disabilities (SEND), the problem becomes even more complex and constrained. In this study, we describe an SBRP formulation capturing SEND specific requirements, catering for heterogeneous fleets, mixed riding, time windows and the use of travel assistants for pupils with special needs. We then present preliminary results investigating the performance of a meta-heuristic and an exact approach, in the context of a real-life case study. Our findings show that when the number of instances increases, exact solvers very rapidly become unworkable. This motivates the development of metaheuristic approaches to this problem. Our results encourage additional research into optimizing school bus routing algorithms to benefit from both precise and heuristic solvers.","PeriodicalId":422029,"journal":{"name":"Proceedings of the Companion Conference on Genetic and Evolutionary Computation","volume":"27 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":"130813410","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
Dynamic Fidelity Selection for Hyperparameter Optimization 超参数优化的动态保真度选择
Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3596320
Shintaro Takenaga, Yoshihiko Ozaki, Masaki Onishi
{"title":"Dynamic Fidelity Selection for Hyperparameter Optimization","authors":"Shintaro Takenaga, Yoshihiko Ozaki, Masaki Onishi","doi":"10.1145/3583133.3596320","DOIUrl":"https://doi.org/10.1145/3583133.3596320","url":null,"abstract":"The dramatic growth of deep learning over the past decade has increased the demand for effective hyperparameter optimization (HPO). At the moment, evolutionary algorithms such as the covariance matrix adaptation evolution strategy (CMA-ES) are recognized as one of the most promising approaches for HPO. However, it is often problematic for practitioners that HPO is a time-consuming task because of its computationally expensive objective even if evaluations were parallelized in each generation of an evolutionary algorithm. To address the problem, multi-fidelity optimization that exploits cheap-to-evaluate lower-fidelity alternatives instead of the true maximum-fidelity objective can be utilized for faster optimization. In this paper, we introduce a new fidelity-selecting strategy designed to solve HPO problems with an evolutionary algorithm. Then, we demonstrate that the CMA-ES with the proposed strategy accelerates the search by about 8.5%--15% compared with the vanilla CMA-ES while keeping the quality of the solutions obtained.","PeriodicalId":422029,"journal":{"name":"Proceedings of the Companion Conference on Genetic and Evolutionary Computation","volume":"57 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":"131642759","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
Optimizing Energy Operation and Planning using Ring Cellular Encode-Decode Univariate Marginal Distribution Algorithm 利用环蜂窝编解码单变量边际分布算法优化能源运行与规划
Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3596422
Ansel Y. Rodríguez González, Angel Díaz Pacheco, Ramón Aranda, Miguel Álvarez Carmona, Yoan Martínez López, Julio Madera
{"title":"Optimizing Energy Operation and Planning using Ring Cellular Encode-Decode Univariate Marginal Distribution Algorithm","authors":"Ansel Y. Rodríguez González, Angel Díaz Pacheco, Ramón Aranda, Miguel Álvarez Carmona, Yoan Martínez López, Julio Madera","doi":"10.1145/3583133.3596422","DOIUrl":"https://doi.org/10.1145/3583133.3596422","url":null,"abstract":"Efficient energy management is critical to building inclusive, safe, resilient, and sustainable cities and human settlements. Optimizing the operation and planning of smart grids is crucial in this regard and remains an active research area. The \"Competition on Evolutionary Computation in the Energy Domain\" has been held annually since 2017. Its 2023 edition focuses on two problems: Risk-based optimization of energy resource management considering the uncertainty of high penetration of distributed energy resources, and Long-term transmission network expansion planning. In this paper, we apply the RCED-UMDA algorithm to solve these problems, and our experimental results demonstrate its superiority over the top three algorithms of the 2022 and 2021 competition editions.","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":"131690056","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
Transfer Learning in Evolutionary Spaces 进化空间中的迁移学习
Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3595038
N. Pillay
{"title":"Transfer Learning in Evolutionary Spaces","authors":"N. Pillay","doi":"10.1145/3583133.3595038","DOIUrl":"https://doi.org/10.1145/3583133.3595038","url":null,"abstract":"","PeriodicalId":422029,"journal":{"name":"Proceedings of the Companion Conference on Genetic and Evolutionary Computation","volume":"39 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":"126819263","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
Multi-Objective Genetic Programming based on Decomposition on Evolving Scheduling Heuristics for Dynamic Scheduling 基于演化调度启发式分解的多目标遗传规划
Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3590582
Meng Xu, Yi Mei, Fangfang Zhang, Mengjie Zhang
{"title":"Multi-Objective Genetic Programming based on Decomposition on Evolving Scheduling Heuristics for Dynamic Scheduling","authors":"Meng Xu, Yi Mei, Fangfang Zhang, Mengjie Zhang","doi":"10.1145/3583133.3590582","DOIUrl":"https://doi.org/10.1145/3583133.3590582","url":null,"abstract":"Dynamic flexible job shop scheduling (DFJSS) is an important combinatorial optimisation problem that requires handling machine assignment and operation sequencing simultaneously in dynamic environments. Genetic programming (GP) has achieved great success to evolve scheduling heuristics for DFJSS. In manufacturing, multi-objective DFJSS (MO-DFJSS) is more common and challenging due to conflicting objectives. Existing Pareto dominance-based multi-objective GP methods show their limitations of not providing good spreadability and consistency in heuristic behaviour. Multi-objective evolutionary algorithm based on decomposition (MOEA/D) has the potential to provide good spreadability and consistency due to the mechanisms of weights-based subproblems decomposition and neighbours-based evolution. However, it is non-trivial to apply MOEA/D to MO-DFJSS since we need to search in heuristic space. To address these challenges, we propose a multi-objective GP approach based on decomposition (MOGP/D) that incorporates the advantages of MOEA/D and GP to learn scheduling heuristics for MO-DFJSS. A mapping strategy is designed to find the fittest individual for each subproblem. Extensive experiments show that MOGP/D obtains competitive performance with the state-of-the-art methods for MO-DFJSS, and good spreadability and consistency in heuristic behaviour.","PeriodicalId":422029,"journal":{"name":"Proceedings of the Companion Conference on Genetic and Evolutionary Computation","volume":"AES-7 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":"126510771","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
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：481959085
Book学术官方微信