Proceedings of the Companion Conference on Genetic and Evolutionary Computation最新文献_第7页

Multi-Objective Artificial Bee Colony for Assembly Flexible Job Shop with Transportation and Setup Times 具有运输和装配时间的装配柔性作业车间多目标人工蜂群

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3590641

Runxin Han, Jun-Qiang Li, Xi Xiao

{"title":"Multi-Objective Artificial Bee Colony for Assembly Flexible Job Shop with Transportation and Setup Times","authors":"Runxin Han, Jun-Qiang Li, Xi Xiao","doi":"10.1145/3583133.3590641","DOIUrl":"https://doi.org/10.1145/3583133.3590641","url":null,"abstract":"In this article, a multi-objective artificial bee colony algorithm with dynamic neighborhood search (MOABC) is employed to address the two-stage assembly flexible job scheduling problem (AFJSP) with transportation and setup times. In the considered problem, there are two stages as follows: 1) in the first stage, the classic flexible job shop scheduling problem (FJSP) with transportation and setup times is considered, and 2) each product is assembled in the second stage, where the setup times between products is embedded. To address the problem, first, a mixed integer linear programming model is developed, wherein makespan and total energy consumption are optimized simultaneously. Second, an effective initialization strategy is designed to generate an initial population with high performance. Next, in the decoding phase, two types of neighborhood knowledge based on the problem characteristics are extracted. Subsequently, to enhance the local search capabilities, a dynamic neighborhood search (DNS) heuristic with five different neighborhood structures in the onlooker stage is proposed. Finally, comprehensive computational comparisons and statistical analysis with state-of-the-art algorithms verified the effectiveness of the proposed algorithm.","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":"122641375","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

Performance Analysis of Hybrid Sampling and Meta-heuristics 混合抽样与元启发式的性能分析

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3590602

Ryan Dieter Lang, A. Engelbrecht

{"title":"Performance Analysis of Hybrid Sampling and Meta-heuristics","authors":"Ryan Dieter Lang, A. Engelbrecht","doi":"10.1145/3583133.3590602","DOIUrl":"https://doi.org/10.1145/3583133.3590602","url":null,"abstract":"This paper investigates the effect of hybridising sampling algorithms with population-based meta-heuristics. Recent literature has shown that alternatives to the traditionally used pseudo-random number generators to generate the initial population of meta-heuristics can improve performance. However, most studies focus on sample sizes that are limited to the size of the initial populations. In contrast, this paper studies the effect of extended random initialisation, which uses relatively large samples and then initialises the meta-heuristics from the points in the sample with the best-found fitness values. A portfolio of three meta-heuristics, four sampling algorithms and three different sampling budgets are analysed from the fixed budget perspective on the BBOB benchmark suite. Statistical analysis of the results shows that the hybrid algorithms converge to better solutions than their non-hybrid counterparts. The results further indicate that large sample sizes can be used to generate landscape analysis features, ensuring reliable approximations of the investigated functions' properties without lessening the meta-heuristics' performance.","PeriodicalId":422029,"journal":{"name":"Proceedings of the Companion Conference on Genetic and Evolutionary Computation","volume":"98 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":"122652797","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

Diversity-driven Multi-population Particle Swarm Optimization for Dynamic Optimization Problem 动态优化问题的多样性驱动多种群粒子群算法

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3590527

Pei-Yao Zhu, Sheng-Hao Wu, Ke-Jing Du, Hua Wang, Jun Zhang, Zhi-hui Zhan

{"title":"Diversity-driven Multi-population Particle Swarm Optimization for Dynamic Optimization Problem","authors":"Pei-Yao Zhu, Sheng-Hao Wu, Ke-Jing Du, Hua Wang, Jun Zhang, Zhi-hui Zhan","doi":"10.1145/3583133.3590527","DOIUrl":"https://doi.org/10.1145/3583133.3590527","url":null,"abstract":"Dynamic optimization problem (DOP) is a kind of problem that contains a series of static problems with different problem characteristics. The main idea of the existing dynamic optimization algorithms is to continuously locate and track changing optimal solutions using limited computational resources. Hence, how to strengthen the exploration ability for locating the optimum of the static problem in an environment and how to improve the adaptation ability to the changing optima in different environments are two key issues for efficiently solving DOP. To address these issues, we propose a diversity-driven multi-population particle swarm optimization (DMPSO) algorithm. First, we propose a center information-based update strategy to strengthen the exploration ability of the PSO algorithm in each subpopulation. Second, a stagnant subpopulation activation strategy is proposed to activate the stagnant subpopulations, and a random walk strategy is proposed to improve the optima tracking capability of the best-performing subpopulation. Third, an archive-based initialization strategy is proposed to reinitialize the population. Experimental studies are conducted on the moving peaks benchmark to compare the DMPSO algorithm with some state-of-the-art dynamic optimization algorithms. The experimental results show that the proposed DMPSO algorithm outperforms the contender algorithms which validate the effectiveness of the proposed algorithm.","PeriodicalId":422029,"journal":{"name":"Proceedings of the Companion Conference on Genetic and Evolutionary Computation","volume":"25 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":"131122382","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

Exploring the Frequency of Boundary Control Methods Activation in Metaheuristic Algorithms 探索元启发式算法中边界控制方法激活的频率

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3596418

T. Kadavy, Michal Pluhacek, Adam Viktorin, R. Šenkeřík

引用次数: 0

Learning to Adapt Genetic Algorithms for Multi-Objective Flexible Job Shop Scheduling Problems 学习适应遗传算法求解多目标柔性作业车间调度问题

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3590700

Robbert Reijnen, Yingqian Zhang, Z. Bukhsh, Mateusz Guzek

引用次数: 0

Applying an evolutionary algorithm to compare fix and flex grid technologies in new generation optical networks 应用进化算法比较新一代光网络中固定网格和柔性网格技术

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3590756

S. Kozdrowski, Pawel Krysztofik

引用次数: 0

An analysis of training models to evolve heuristics for the travelling salesman problem 旅行推销员问题的演化启发式训练模型分析

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3590559

Francisco Javier Gil Gala, Marko Durasevic, Mateja Dumic, Rebeka Čorić, D. Jakobović

引用次数: 0

Evolving Robust Facility Placements 不断发展的稳健设施布局

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3590712

W. Thompson, Alex Freidrichsen, C. Danforth, P. Dodds, Nicholas Cheney

引用次数: 0

A New Genetic Programming Representation for Feature Learning in Skin Cancer Detection 皮肤癌检测中特征学习的一种新的遗传规划表示

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3590550

Q. Ain, Harith Al-Sahaf, Bing Xue, Mengjie Zhang

引用次数: 0

Comparing Individual Representations in Grammar-Guided Genetic Programming for Glucose Prediction in People with Diabetes 比较语法引导遗传规划在糖尿病患者血糖预测中的个体表征

Proceedings of the Companion Conference on Genetic and Evolutionary Computation Pub Date : 2023-07-15 DOI: 10.1145/3583133.3596315

L. Ingelse, J. Hidalgo, J. Colmenar, Nuno Lourenço, Alcides Fonseca

{"title":"Comparing Individual Representations in Grammar-Guided Genetic Programming for Glucose Prediction in People with Diabetes","authors":"L. Ingelse, J. Hidalgo, J. Colmenar, Nuno Lourenço, Alcides Fonseca","doi":"10.1145/3583133.3596315","DOIUrl":"https://doi.org/10.1145/3583133.3596315","url":null,"abstract":"The representation of individuals in Genetic Programming (GP) has a large impact on the evolutionary process. In this work, we investigate the evolutionary process of three Grammar-Guided GP (GGGP) methods, Context-Free Grammars GP (CFG-GP), Grammatical Evolution (GE) and Structured Grammatical Evolution (SGE), in the context of the complex, real-world problem of predicting the glucose level of people with diabetes two hours ahead of time. Our analysis differs from previous analyses by (1) comparing all three methods on a complex benchmark, (2) implementing the methods in the same framework, allowing a fairer comparison, and (3) analyzing the evolutionary process outside of performance. We conclude that representation choice is more impactful with a higher maximum depth, and that CFG-GP better explores the search space for deeper trees, achieving better results. Furthermore, we find that CFG-GP relies more on feature construction, whereas GE and SGE rely more on feature selection. Finally, we altered the GGGP methods in two ways: using ε-lexicase selection, which solved the overfitting problem of CFG-GP; and with a penalization of complex trees, to create more interpretable trees. Combining ε-lexicase selection with CFG-GP performed best.","PeriodicalId":422029,"journal":{"name":"Proceedings of the Companion Conference on Genetic and Evolutionary Computation","volume":"77 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":"133534825","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