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

Tomographic Reconstruction with Search Space Expansion 搜索空间扩展的层析重建

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590372

M. M. al-Rifaie, T. Blackwell

引用次数: 0

Divide and conquer: Using single objective dispatching rules to improve convergence for multi-objective optimisation 分而治之:使用单目标调度规则来提高多目标优化的收敛性

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590370

Marko Durasevic, F. Gil-Gala, D. Jakobović

{"title":"Divide and conquer: Using single objective dispatching rules to improve convergence for multi-objective optimisation","authors":"Marko Durasevic, F. Gil-Gala, D. Jakobović","doi":"10.1145/3583131.3590370","DOIUrl":"https://doi.org/10.1145/3583131.3590370","url":null,"abstract":"Dynamic multi-objective (MO) scheduling problems are encountered in various real-world situations. Due to dynamic events that occur in such problems, one has to resort to using simple constructive heuristics, called dispatching rules (DRs), when tackling them. Since DRs are difficult to design manually there is a lack of existing DRs suitable for solving MO problems. Due to that reason, genetic programming has successfully been applied to evolve DRs specifically for solving MO problems. The process of evolving new DRs is computationally expensive, requiring a significant amount of time to obtain DRs of good quality. For that reason it is worth investigating inwhich ways the convergence of algorithms could be improved. One option is to use DRs previously evolved for optimising individual criteria to initialise the starting population when optimising a MO problem. The goal of this study is to investigate how such an initialisation strategy affects the performance of NSGA-II and NSGA-III when evolving DRs for MO problems. Therefore, 8 MO unrelated machines scheduling problems, containing between 2 and 5 criteria, are considered. The obtained results demonstrate that using previously evolved DRs for single objective optimisation leads to a faster convergence, and in many cases significantly better results.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"78 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80850594","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

Differentiable Soft-Robot Generation 可微软机器人生成

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590408

François Cochevelou, D. Bonner, Martin-Pierre Schmidt

引用次数: 2

A Double Lexicase Selection Operator for Bloat Control in Evolutionary Feature Construction for Regression 回归进化特征构建中膨胀控制的双词法选择算子

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590365

Hengzhe Zhang, Qi Chen, Bing Xue, W. Banzhaf, Mengjie Zhang

{"title":"A Double Lexicase Selection Operator for Bloat Control in Evolutionary Feature Construction for Regression","authors":"Hengzhe Zhang, Qi Chen, Bing Xue, W. Banzhaf, Mengjie Zhang","doi":"10.1145/3583131.3590365","DOIUrl":"https://doi.org/10.1145/3583131.3590365","url":null,"abstract":"Evolutionary feature construction is an important technique in the machine learning domain for enhancing learning performance. However, traditional genetic programming-based feature construction methods often suffer from bloat, which means the sizes of constructed features increase excessively without improved performance. To address this issue, this paper proposes a double-stage lexicase selection operator to control bloat while not damaging search effectiveness. This new operator contains a two-stage selection process, where the first stage selects individuals based on fitness values and the second stage selects individuals based on tree sizes. Therefore, the proposed operator can control bloat meanwhile leveraging the advantage of the lexicase selection operator. Experimental results on 98 regression datasets show that compared to the traditional bloat control method of having a depth limit, the proposed selection operator not only significantly reduces the sizes of constructed features on all datasets but also keeps a similar level of predictive performance. A comparative experiment with seven bloat control methods shows that the double lexicase selection operator achieves the best trade-off between the model performance and the model size.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78927003","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

Particle Swarm Optimization with Ring Topology for Multi-modal Multi-objective Problems 多模态多目标问题的环拓扑粒子群优化

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590382

You-Chao Sun, Chaoli Sun

{"title":"Particle Swarm Optimization with Ring Topology for Multi-modal Multi-objective Problems","authors":"You-Chao Sun, Chaoli Sun","doi":"10.1145/3583131.3590382","DOIUrl":"https://doi.org/10.1145/3583131.3590382","url":null,"abstract":"Multi-modal multi-objective optimization problems (MMOPs) are ubiquitous in real-world applications, in which multiple objective functions are conflicting and need to be optimized simultaneously. Furthermore, multiple Pareto optimal solutions of MMOPs are mapped to the same point on the Pareto front. Canonical multi-objective optimization algorithms show poor performance for MMOPs due to the lack of diversity maintenance in the decision space. In this paper, a particle swarm optimization with ring topology, denoted as PSO-RT, is proposed for solving MMOPs. The population is firstly divided into several sub-populations based on a dynamic radius to form a ring topology. Then, each solution will update its position by learning from one of its personal best positions and the best position of its neighbors. The personal best position to be learned is selected based on the special crowding distance to improve the exploration capability, and the best position of its neighbors is selected according to the weighted indicator to speed up convergence. The performance of PSO-RT is evaluated on 22 test problems. Experimental results show that our proposed PSO-RT can obtain competitive or better results than some state-of-the-art algorithms proposed for MMOPs in terms of Pareto sets proximity (PSP) and inverted generational distance (IGDX) matrics.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"632 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76809581","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

The Impact of Morphological Diversity in Robot Swarms 机器人群体形态多样性的影响

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590347

G. Nitschke, S. Mkhatshwa

引用次数: 0

Learning Synergies for Multi-Objective Optimization in Asymmetric Multiagent Systems 非对称多智能体系统多目标优化的学习协同

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590524

Gaurav Dixit, Kagan Tumer

{"title":"Learning Synergies for Multi-Objective Optimization in Asymmetric Multiagent Systems","authors":"Gaurav Dixit, Kagan Tumer","doi":"10.1145/3583131.3590524","DOIUrl":"https://doi.org/10.1145/3583131.3590524","url":null,"abstract":"Agents in a multiagent system must learn diverse policies that allow them to express complex inter-agent relationships required to optimize a single team objective. Multiagent Quality Diversity methods partially address this by transforming the agents' large joint policy space to a tractable sub-space that can produce synergistic agent policies. However, a majority of real-world problems are inherently multi-objective and require asymmetric agents (agents with different capabilities and objectives) to learn policies that represent diverse trade-offs between agent-specific and team objectives. This work introduces Multi-objective Asymmetric Island Model (MO-AIM), a multi-objective multiagent learning framework for the discovery of generalizable agent synergies and trade-offs that is based on adapting the population dynamics over a spectrum of tasks. The key insight is that the competitive pressure arising from the changing populations on the team tasks forces agents to acquire robust synergies required to balance their individual and team objectives in response to the nature of their teams and task dynamics. Results on several variations of a multi-objective habitat problem highlight the potential of MO-AIM in producing teams with diverse specializations and trade-offs that readily adapt to unseen tasks.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"89 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87413774","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

Learning to Select Initialisation Heuristic for Vehicle Routing Problems 车辆路径问题的初始化启发式选择学习

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590397

Joao Guilherme Cavalcanti Costa, Yi Mei, Mengjie Zhang

{"title":"Learning to Select Initialisation Heuristic for Vehicle Routing Problems","authors":"Joao Guilherme Cavalcanti Costa, Yi Mei, Mengjie Zhang","doi":"10.1145/3583131.3590397","DOIUrl":"https://doi.org/10.1145/3583131.3590397","url":null,"abstract":"The Vehicle Routing Problem (VRP) is a complex problem that comes with a great number of applications in logistics and supply chains. It is non-trivial to select the optimal VRP techniques to solve these applications, especially since there are several possible scenarios. As there is no way to predict how each algorithm would perform until it is (at least partially) deployed, it would make sense in selecting the ones that have higher adaptability to the given environment. In this paper, we consider this idea on the initialisation part of a local search-based metaheuristic. We argue that a proper initialisation is important for obtaining better VRP solutions and apply several machine learning techniques aiming to learn how to use distinct features from four commonly used construction heuristics solutions, predicting the scenarios in which they are the most effective. We also provide relevant discussions on the effects of the initial solution on a local search context. Results show that the proposed method can help select the best or an improving method for the majority of the instances considered, especially for large-scale VRP instances.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88058448","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

Runtime Analysis with Variable Cost 具有可变成本的运行时分析

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590432

P. Lehre, Andrew M. Sutton

{"title":"Runtime Analysis with Variable Cost","authors":"P. Lehre, Andrew M. Sutton","doi":"10.1145/3583131.3590432","DOIUrl":"https://doi.org/10.1145/3583131.3590432","url":null,"abstract":"The usual approach in runtime analysis is to derive estimates on the number of fitness function evaluations required by a method until a suitable element of the search space is found. One justification for this is that in real applications, fitness evaluation often contributes the most computational effort. A tacit assumption in this approach is that this effort is uniform and static across the search space. However, this assumption often does not hold in practice: some candidates may be far more expensive to evaluate than others. This might occur, for example, when fitness evaluation requires running a simulation or training a machine learning model. Despite the availability of a wide range of benchmark functions coupled with various runtime performance guarantees, the runtime analysis community currently lacks a solid perspective of handling variable fitness cost. Our goal with this paper is to argue for incorporating this perspective into our theoretical toolbox. We introduce two models of handling variable cost: a simple non-adaptive model together with a more general adaptive model. We prove cost bounds in these scenarios and discuss the implications for taking into account costly regions in the search space.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91295655","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 fairness tradeoffs in machine learning with multiobjective meta-models 基于多目标元模型的机器学习公平性优化

Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference Pub Date : 2023-07-12 DOI: 10.1145/3583131.3590487

William G La Cava

{"title":"Optimizing fairness tradeoffs in machine learning with multiobjective meta-models","authors":"William G La Cava","doi":"10.1145/3583131.3590487","DOIUrl":"https://doi.org/10.1145/3583131.3590487","url":null,"abstract":"Improving the fairness of machine learning models is a nuanced task that requires decision makers to reason about multiple, conflicting criteria. The majority of fair machine learning methods transform the error-fairness trade-off into a single objective problem with a parameter controlling the relative importance of error versus fairness. We propose instead to directly optimize the error-fairness tradeoff by using multi-objective optimization. We present a flexible framework for defining the fair machine learning task as a weighted classification problem with multiple cost functions. This framework is agnostic to the underlying prediction model as well as the metrics. We use multiobjective optimization to define the sample weights used in model training for a given machine learner, and adapt the weights to optimize multiple metrics of fairness and accuracy across a set of tasks. To reduce the number of optimized parameters, and to constrain their complexity with respect to population subgroups, we propose a novel meta-model approach that learns to map protected attributes to sample weights, rather than optimizing those weights directly. On a set of real-world problems, this approach outperforms current state-of-the-art methods by finding solution sets with preferable error/fairness trade-offs.","PeriodicalId":89841,"journal":{"name":"Proceedings of the Genetic and Evolutionary Computation Conference. Genetic and Evolutionary Computation Conference","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84154180","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