Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference最新文献_第5页

Genetic Algorithm with Linkage Learning 链接学习的遗传算法

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590349

R. Tinós, M. Przewozniczek, L. D. Whitley, F. Chicano

{"title":"Genetic Algorithm with Linkage Learning","authors":"R. Tinós, M. Przewozniczek, L. D. Whitley, F. Chicano","doi":"10.1145/3583131.3590349","DOIUrl":"https://doi.org/10.1145/3583131.3590349","url":null,"abstract":"Next-generation genetic algorithms (GAs) should explore information from the problem structure whenever possible. Variable interactions can be inferred using linkage learning. Statistical linkage learning techniques were shown to improve GAs' effectiveness significantly in many problems, but may eventually report false linkages. On the other hand, empirical linkage learning (ELL) techniques discover only true variable dependencies. However, traditional ELL techniques are computationally expensive. We introduce the genetic algorithm with linkage learning (GAwLL), which discovers an empirical weighted variable interaction graph (VIGw) as a side-effect of the optimization performed by a GA, making it a no-cost ELL technique. Vertices of the VIGw represent decision variables and weights indicate the strength of the interaction between variables. The VIGw allows us to obtain new insights about the optimization problem and can be used to design genetic operators that efficiently explore the information about variable dependencies. Experiments with NK landscapes show that GAwLL is able to efficiently build the empirical VIGw. We also present an interesting machine learning application, where the VIGw represents a feature interaction network. By using GAwLL, the feature interaction network is built as a side-effect of evolutionary feature selection.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78822977","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

Towards Evolutionary Control Laws for Viability Problems 关于生存问题的进化控制律

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590415

A. Tonda, I. Alvarez, Sophie Martin, Giovanni Squillero, E. Lutton

{"title":"Towards Evolutionary Control Laws for Viability Problems","authors":"A. Tonda, I. Alvarez, Sophie Martin, Giovanni Squillero, E. Lutton","doi":"10.1145/3583131.3590415","DOIUrl":"https://doi.org/10.1145/3583131.3590415","url":null,"abstract":"The mathematical theory of viability, developed to formalize problems related to natural and social phenomena, investigates the evolution of dynamical systems under constraints. A main objective of this theory is to design control laws to keep systems inside viable domains. Control laws are traditionally defined as rules, based on the current position in the state space with respect to the boundaries of the viability kernel. However, finding these boundaries is a computationally expensive procedure, feasible only for trivial systems. We propose an approach based on Genetic Programming (GP) to discover control laws for viability problems in analytic form. Such laws could keep a system viable without the need of computing its viability kernel, facilitate communication with stakeholders, and improve explainability. A candidate set of control rules is encoded as GP trees describing equations. Evaluation is noisy, due to stochastic sampling: initial conditions are randomly drawn from the state space of the problem, and for each, a system of differential equations describing the system is solved, creating a trajectory. Candidate control laws are rewarded for keeping viable as many trajectories as possible, for as long as possible. The proposed approach is evaluated on established benchmarks for viability and delivers promising results.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"13 10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90759819","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 Optimization for the Detection and Concealment of Spatially Defined Communication Networks 空间定义通信网络检测与隐藏的分集优化

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590405

Aneta Neumann, Sharlotte Gounder, Xiankun Yan, Gregory Sherman, Benjamin Campbell, Mingyu Guo, F. Neumann

{"title":"Diversity Optimization for the Detection and Concealment of Spatially Defined Communication Networks","authors":"Aneta Neumann, Sharlotte Gounder, Xiankun Yan, Gregory Sherman, Benjamin Campbell, Mingyu Guo, F. Neumann","doi":"10.1145/3583131.3590405","DOIUrl":"https://doi.org/10.1145/3583131.3590405","url":null,"abstract":"In recent years, computing diverse sets of high quality solutions for an optimization problem has become an important topic. The goal of computing diverse sets of high quality solutions is to provide a variety of options to decision makers, allowing them to choose the best solution for their particular problem. We consider the problem of constructing a wireless communication network for a given set of entities. Our goal is to minimize the area covered by the senders' transmissions while also avoiding adversaries that may observe the communication. We provide evolutionary diversity optimization (EDO) algorithms for this problem. We provide a formulation based on minimum spanning forests that are used as a representation and show how this formulation can be turned into a wireless communication network that avoids a given set of adversaries. We evaluate our EDO approach based on a number of benchmark instances and compare the diversity of the obtained populations in respect to the quality criterion of the given solutions as well as the chosen algorithm parameters. Our results demonstrate the effectiveness of our EDO approaches for the detection and concealment of communication networks both in terms of the quality and the diversity of the obtained solutions.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"33 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89359233","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: 1

Bayesian Quality Diversity Search with Interactive Illumination 基于交互照明的贝叶斯质量多样性搜索

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590486

P. Kent, Juergen Branke

{"title":"Bayesian Quality Diversity Search with Interactive Illumination","authors":"P. Kent, Juergen Branke","doi":"10.1145/3583131.3590486","DOIUrl":"https://doi.org/10.1145/3583131.3590486","url":null,"abstract":"This paper presents a novel way for interactively identifying a most preferable solution based on quality and behavioural characteristics. Our algorithm combines the principles of Quality-Diversity Search and Bayesian Optimization to create Gaussian Process surrogate models of the behaviour and fitness space. Unlike traditional Quality-Diversity methods which aim to find good solutions with different behavioural characteristics, we propose a three-step interactive approach that allows a decision maker to efficiently identify the most preferred solution(s). In the first stage, it uses an entropy-based acquisition function to generate an illumination model, followed by an interactive phase where the decision maker can specify regions of interest and a target behaviour. These preferences are then utilized by an improvement greedy acquisition function to guide the optimization process and quickly identify a solution close to the user-specified target. In a case study, with a simulated decision maker, we demonstrate that our approach can find better solutions much more quickly than by selecting the most preferred solution from an archive generated with MAP-Elites.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"201 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85782759","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: 1

Multi-Objective Multi-Gene Genetic Programming for the Prediction of Leakage in Water Distribution Networks 配水管网泄漏预测的多目标多基因遗传规划

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590499

Matthew Hayslep, E. Keedwell, R. Farmani

{"title":"Multi-Objective Multi-Gene Genetic Programming for the Prediction of Leakage in Water Distribution Networks","authors":"Matthew Hayslep, E. Keedwell, R. Farmani","doi":"10.1145/3583131.3590499","DOIUrl":"https://doi.org/10.1145/3583131.3590499","url":null,"abstract":"Understanding leakage is an important challenge within the water sector to minimise waste, energy use and carbon emissions. Every Water Distribution Network (WDN) has leakage, usually approximated as Minimum Night Flow (MNF) for each District Metered Area (DMA). However, not all DMAs have instruments to monitor leakage directly, or the main dynamic factors that contribute to it. Therefore, this article will estimate the leakage of a DMA by using the recorded features of its pipes, making use of readily available asset data collected routinely by water companies. This article interprets this problem as a feature construction task and uses a multi-objective multi-gene strongly typed genetic programming approach to create a set of features. These features are used by a linear regression model to estimate the average long-term leakage in DMAs and Shapley values are used to understand the impact and importance of each tree. The methodology is applied to a dataset for a real-world WDN with over 700 DMAs and the results are compared to a previous work which used human-constructed features. The results show comparable performance with significantly fewer, and less complex features. In addition, novel features are found that were not part of the human-constructed features.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"49 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76005772","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

A Fast Multi-objective Evolutionary Approach for Designing Large-Scale Optical Mode Sorter 大型光学模式分选器设计的快速多目标进化方法

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590479

Annibale Panichella, G. D. Domenico

{"title":"A Fast Multi-objective Evolutionary Approach for Designing Large-Scale Optical Mode Sorter","authors":"Annibale Panichella, G. D. Domenico","doi":"10.1145/3583131.3590479","DOIUrl":"https://doi.org/10.1145/3583131.3590479","url":null,"abstract":"Spatial mode division de-multiplexing of optical signals has many real-world applications, such as quantum computing and both classical and quantum optical communication. In this context, it is crucial to develop devices able to efficiently sort optical signals according to the optical mode they belong to and route them on different paths. Depending on the mode selected, this problem can be very hard to tackle. Recently, researchers have proposed using multi-objective evolutionary algorithms (MOEAs) ---and NSGA-II in particular--- combined with Linkage Learning (LL) to automate the process of design mode sorter. However, given the very large-search scale of the problem, the existing evolutionary-based solutions have a very slow convergence rate. In this paper, we proposed a novel approach for mode sorter design that combines (1) stochastic linkage learning, (2) the adaptive geometry estimation-based MOEA (AGE-MOEA-II), and (3) an adaptive mutation operator. Our experiments with two- and three-objectives (beams) show that our approach is faster (better convergence rate) and produces better mode sorters (closer to the ideal solutions) than the state-of-the-art approach. A direct comparison with the vanilla NSGA-II and AGE-MOEA-II also further confirms the importance of adopting LL in this domain.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"81 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79072316","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

Enhanced Strongly typed Genetic Programming for Algorithmic Trading 算法交易的增强强类型遗传规划

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590359

Eva Christodoulaki, Michael Kampouridis, M. Kyropoulou

{"title":"Enhanced Strongly typed Genetic Programming for Algorithmic Trading","authors":"Eva Christodoulaki, Michael Kampouridis, M. Kyropoulou","doi":"10.1145/3583131.3590359","DOIUrl":"https://doi.org/10.1145/3583131.3590359","url":null,"abstract":"This paper proposes a novel strongly typed Genetic Programming (STGP) algorithm that combines Technical (TA) and Sentiment analysis (SA) indicators to produce trading strategies. While TA and SA have been successful when used individually, their combination has not been considered extensively. Our proposed STGP algorithm has a novel fitness function, which rewards not only a tree's trading performance, but also the trading performance of its TA and SA subtrees. To achieve this, the fitness function is equal to the sum of three components: the fitness function for the complete tree, the fitness function of the TA subtree, and the fitness function of the SA subtree. In doing so, we ensure that the evolved trees contain profitable trading strategies that take full advantage of both technical and sentiment analysis. We run experiments on 35 international stocks and compare the STGP's performance to four other GP algorithms, as well as multilayer perceptron, support vector machines, and buy and hold. Results show that the proposed GP algorithm statistically and significantly outperforms all benchmarks and it improves the financial performance of the trading strategies produced by other GP algorithms by up to a factor of two for the median rate of return.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80373261","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

Generating diverse and discriminatory knapsack instances by searching for novelty in variable dimensions of feature-space 在变维特征空间中搜索新颖性，生成多样性和歧视性背包实例

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590504

Alejandro Marrero, Eduardo Segredo, E. Hart, Jakob Bossek, Aneta Neumann

{"title":"Generating diverse and discriminatory knapsack instances by searching for novelty in variable dimensions of feature-space","authors":"Alejandro Marrero, Eduardo Segredo, E. Hart, Jakob Bossek, Aneta Neumann","doi":"10.1145/3583131.3590504","DOIUrl":"https://doi.org/10.1145/3583131.3590504","url":null,"abstract":"Generating new instances via evolutionary methods is commonly used to create new benchmarking data-sets, with a focus on attempting to cover an instance-space as completely as possible. Recent approaches have exploited Quality-Diversity methods to evolve sets of instances that are both diverse and discriminatory with respect to a portfolio of solvers, but these methods can be challenging when attempting to find diversity in a high-dimensional feature-space. We address this issue by training a model based on Principal Component Analysis on existing instances to create a low-dimension projection of the high-dimension feature-vectors, and then apply Novelty Search directly in the new low-dimension space. We conduct experiments to evolve diverse and discriminatory instances of Knapsack Problems, comparing the use of Novelty Search in the original feature-space to using Novelty Search in a low-dimensional projection, and repeat over a given set of dimensions. We find that the methods are complementary: if treated as an ensemble, they collectively provide increased coverage of the space. Specifically, searching for novelty in a low-dimension space contributes 56% of the filled regions of the space, while searching directly in the feature-space covers the remaining 44%.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89191678","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

Sample-Aware Surrogate-Assisted Genetic Programming for Scheduling Heuristics Learning in Dynamic Flexible Job Shop Scheduling 基于样本感知代理辅助遗传规划的动态柔性作业车间调度启发式学习

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590440

Luyao Zhu, Fangfang Zhang, Xiaodong Zhu, Ke Chen, Mengjie Zhang

{"title":"Sample-Aware Surrogate-Assisted Genetic Programming for Scheduling Heuristics Learning in Dynamic Flexible Job Shop Scheduling","authors":"Luyao Zhu, Fangfang Zhang, Xiaodong Zhu, Ke Chen, Mengjie Zhang","doi":"10.1145/3583131.3590440","DOIUrl":"https://doi.org/10.1145/3583131.3590440","url":null,"abstract":"Genetic programming (GP) has been successfully introduced to learn scheduling heuristics for dynamic flexible job shop scheduling (DFJSS) automatically. However, the evaluations of GP individuals are normally time-consuming, especially with long DFJSS simulations. Taking k-nearest neighbour with phenotypic characterisations of GP individuals as a surrogate approach, has been successfully used to preselect GP offspring to the next generation for effectiveness improvement. However, this approach is not straightforward to improve the training efficiency, which is normally the primary goal of surrogate. In addition, there is no study on which GP individuals (samples) are good for building surrogate models. To this end, first, this paper proposes a surrogate-assisted GP algorithm to reduce the training time of learning scheduling heuristics for DFJSS. Second, this paper further proposes an effective sampling strategy for surrogate-assisted GP. The results show that our proposed algorithm can achieve comparable performance with only about a third of training time of traditional GP. With the same training time, the proposed algorithm can significantly improve the quality of learned scheduling heuristics in all examined scenarios. Furthermore, the evolved scheduling heuristics by the proposed sample-aware surrogate-assisted GP are more interpretable with smaller rule sizes than traditional GP.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"288 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86422517","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

Trade-off Between Robustness and Worst-Case Performance in Min-Max Optimization 最小-最大优化中鲁棒性与最坏情况性能的权衡

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590362

Hinata Edo, Yoshiki Miyauchi, A. Maki, Youhei Akimoto

{"title":"Trade-off Between Robustness and Worst-Case Performance in Min-Max Optimization","authors":"Hinata Edo, Yoshiki Miyauchi, A. Maki, Youhei Akimoto","doi":"10.1145/3583131.3590362","DOIUrl":"https://doi.org/10.1145/3583131.3590362","url":null,"abstract":"Min-max optimization is an approach to avoid the risk of obtaining a solution whose performance is satisfactory in a simulation scenario but significantly degraded in real-world scenarios, thereby obtaining a robust solution under the worst case in the considered scenarios. However, owing to the trade-off between robustness and worst-case performance, it is often difficult to design a set of scenarios in simulation-based optimization, known as the uncertainty set, which can be a practical barrier to applying min-max optimization. In this study, we propose a novel approach to obtaining the Pareto front of the bi-objective optimization for the size of the uncertainty set and worst-case performance under the uncertainty set. The proposed approach aims to obtain a set of solutions that cover the worst-case performance effectively but are better than a given performance threshold. The effectiveness of each algorithmic component was evaluated through numerical experiments using a synthetic problem. Applications to automatic ship berthing tasks demonstrate the usefulness of the proposed approach and its limitations.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87554780","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