Proceedings of the 2020 Genetic and Evolutionary Computation Conference最新文献

Scalability analysis of grammatical evolution based test data generation 基于测试数据生成的语法演化可扩展性分析

Proceedings of the 2020 Genetic and Evolutionary Computation Conference Pub Date : 2020-06-25 DOI: 10.1145/3377930.3390167

M. S. Anjum, C. Ryan

{"title":"Scalability analysis of grammatical evolution based test data generation","authors":"M. S. Anjum, C. Ryan","doi":"10.1145/3377930.3390167","DOIUrl":"https://doi.org/10.1145/3377930.3390167","url":null,"abstract":"Heuristic-based search techniques have been increasingly used to automate different aspects of software testing. Several studies suggest that variable interdependencies may exist in branching conditions of real-life programs, and these dependencies result in the need for highly precise data values (such as of the form i=j=k) for code coverage analysis. This requirement makes it very difficult for Genetic Algorithm (GA)-based approach to successfully search for the required test data from vast search spaces of real-life programs. Ariadne is the only Grammatical Evolution (GE)-based test data generation system, proposed to date, that uses grammars to exploit variable interdependencies to improve code coverage. Ariadne has been compared favourably to other well-known test data generation techniques in the literature; however, its scalability has not yet been tested for increasingly complex programs. This paper presents the results of a rigorous analysis performed to examine Ariadne's scalability. We also designed and employed a large set of highly scalable 18 benchmark programs for our experiments. Our results suggest that Ariadne is highly scalable as it exhibited 100% coverage across all the programs of increasing complexity with significantly smaller search costs than GA-based approaches, which failed even with huge search budgets.","PeriodicalId":203136,"journal":{"name":"Proceedings of the 2020 Genetic and Evolutionary Computation Conference","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115692616","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

COUGAR 美洲狮

Proceedings of the 2020 Genetic and Evolutionary Computation Conference Pub Date : 2020-06-25 DOI: 10.1145/3377930.3390151

Zachary Wilkins, Nur Zincir-Heywood

引用次数: 4

Towards sustainable forest management strategies with MOEAs 与moea一起制定可持续森林管理战略

Proceedings of the 2020 Genetic and Evolutionary Computation Conference Pub Date : 2020-06-25 DOI: 10.1145/3377930.3389837

Philipp Back, Antti-Jussi Suominen, P. Malo, O. Tahvonen, Julian Blank, K. Deb

{"title":"Towards sustainable forest management strategies with MOEAs","authors":"Philipp Back, Antti-Jussi Suominen, P. Malo, O. Tahvonen, Julian Blank, K. Deb","doi":"10.1145/3377930.3389837","DOIUrl":"https://doi.org/10.1145/3377930.3389837","url":null,"abstract":"Sustainable forest management is a crucial element in combating climate change, plastic pollution, and other unsolved challenges of the 21st century. Forests not only produce wood - a renewable resource that is increasingly replacing fossil-based materials - but also preserve biodiversity and store massive amounts of carbon. Thus, a truly optimal forest policy has to balance profit-oriented logging with ecological and societal interests, and should thus be solved as a multi-objective optimization problem. Economic forest research, however, has largely focused on profit maximization. Recent publications still scalarize the problem a priori by assigning weights to objectives. In this paper, we formulate a multi-objective forest management problem where profit, carbon storage, and biodiversity are maximized. We obtain Pareto-efficient forest management strategies by utilizing three state-of-the-art Multi-Objective Evolutionary Algorithms (MOEAs), and by incorporating domain-specific knowledge through customized evolutionary operators. An analysis of Pareto-efficient strategies and their harvesting schedules in the design space clearly shows the benefits of the proposed approach. Unlike many EMO application studies, we demonstrate how a systematic post-optimality trade-off analysis can be applied to choose a single preferred solution. Our pioneering work on sustainable forest management explores an entirely new application area for MOEAs with great societal impact.","PeriodicalId":203136,"journal":{"name":"Proceedings of the 2020 Genetic and Evolutionary Computation Conference","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123441937","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: 3

Exploring the evolution of GANs through quality diversity 通过质量多样性探索gan的进化

Proceedings of the 2020 Genetic and Evolutionary Computation Conference Pub Date : 2020-06-25 DOI: 10.1145/3377930.3389824

Victor Costa, Nuno Lourenço, João Correia, P. Machado

{"title":"Exploring the evolution of GANs through quality diversity","authors":"Victor Costa, Nuno Lourenço, João Correia, P. Machado","doi":"10.1145/3377930.3389824","DOIUrl":"https://doi.org/10.1145/3377930.3389824","url":null,"abstract":"Generative adversarial networks (GANs) achieved relevant advances in the field of generative algorithms, presenting high-quality results mainly in the context of images. However, GANs are hard to train, and several aspects of the model should be previously designed by hand to ensure training success. In this context, evolutionary algorithms such as COEGAN were proposed to solve the challenges in GAN training. Nevertheless, the lack of diversity and premature optimization can be found in some of these solutions. We propose in this paper the application of a quality-diversity algorithm in the evolution of GANs. The solution is based on the Novelty Search with Local Competition (NSLC) algorithm, adapting the concepts used in COEGAN to this new proposal. We compare our proposal with the original COEGAN model and with an alternative version using a global competition approach. The experimental results evidenced that our proposal increases the diversity of the discovered solutions and leverage the performance of the models found by the algorithm. Furthermore, the global competition approach was able to consistently find better models for GANs.","PeriodicalId":203136,"journal":{"name":"Proceedings of the 2020 Genetic and Evolutionary Computation Conference","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129868620","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: 12

Constraint handling within MOEA/D through an additional scalarizing function 通过附加的缩放函数约束MOEA/D内的处理

Proceedings of the 2020 Genetic and Evolutionary Computation Conference Pub Date : 2020-06-25 DOI: 10.1145/3377930.3390240

Saúl Zapotecas-Martínez, Antonin Ponsich

引用次数: 7

Distributed random walks for fitness landscape analysis 适合度景观分析的分布随机漫步

Proceedings of the 2020 Genetic and Evolutionary Computation Conference Pub Date : 2020-06-25 DOI: 10.1145/3377930.3389829

Ryan Dieter Lang, A. Engelbrecht

引用次数: 1

Constructing efficient multigrid solvers with genetic programming 用遗传规划构造高效的多网格求解器

Proceedings of the 2020 Genetic and Evolutionary Computation Conference Pub Date : 2020-06-25 DOI: 10.1145/3377930.3389811

J. Schmitt, S. Kuckuk, H. Köstler

{"title":"Constructing efficient multigrid solvers with genetic programming","authors":"J. Schmitt, S. Kuckuk, H. Köstler","doi":"10.1145/3377930.3389811","DOIUrl":"https://doi.org/10.1145/3377930.3389811","url":null,"abstract":"For many linear and nonlinear systems that arise from the discretization of partial differential equations the construction of an efficient multigrid solver is a challenging task. Here we present a novel approach for the optimization of geometric multigrid methods that is based on evolutionary computation, a generic program optimization technique inspired by the principle of natural evolution. A multigrid solver is represented as a tree of mathematical expressions which we generate based on a tailored grammar. The quality of each solver is evaluated in terms of convergence and compute performance using automated local Fourier analysis (LFA) and roofline performance modeling, respectively. Based on these objectives a multi-objective optimization is performed using grammar-guided genetic programming with a non-dominated sorting based selection. To evaluate the model-based prediction and to target concrete applications, scalable implementations of an evolved solver can be automatically generated with the ExaStencils framework. We demonstrate the effectiveness of our approach by constructing multigrid solvers for a linear elastic boundary value problem that are competitive with common V- and W-cycles.","PeriodicalId":203136,"journal":{"name":"Proceedings of the 2020 Genetic and Evolutionary Computation Conference","volume":"11 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123684007","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: 6

Exploratory path planning for mobile robots in dynamic environments with ant colony optimization 基于蚁群优化的动态环境下移动机器人探索性路径规划

Proceedings of the 2020 Genetic and Evolutionary Computation Conference Pub Date : 2020-06-25 DOI: 10.1145/3377930.3390219

V. Santos, F. E. B. Otero, Colin G. Johnson, F. Osório, C. Toledo

引用次数: 6

Adaptive weighted splines: a new representation to genetic programming for symbolic regression 自适应加权样条:符号回归遗传规划的一种新表示

Proceedings of the 2020 Genetic and Evolutionary Computation Conference Pub Date : 2020-06-25 DOI: 10.1145/3377930.3390244

Christian Raymond, Qi Chen, Bing Xue, Mengjie Zhang

{"title":"Adaptive weighted splines: a new representation to genetic programming for symbolic regression","authors":"Christian Raymond, Qi Chen, Bing Xue, Mengjie Zhang","doi":"10.1145/3377930.3390244","DOIUrl":"https://doi.org/10.1145/3377930.3390244","url":null,"abstract":"Genetic Programming for Symbolic Regression is often prone to overfit the training data, resulting in poor generalization on unseen data. To address this issue, many pieces of research have been devoted to regularization via controlling the model complexity. However, due to the unstructured tree based representation of individuals the model complexity cannot be directly computed, rather approximation of the complexity must be taken. This paper proposes a new novel representation called Adaptive Weighted Splines which enables explicit control over the complexity of individuals using splines. The experimental results confirm that this new representation is significantly better than the tree-based representation at avoiding overfitting and generalizing on unseen data, demonstrating notably better and far more consistent generalization performances on all the benchmark problems. Further analysis also shows that in most cases, the new Genetic Programming method outperforms classical regression techniques such as Linear Regression, Support Vector Regression, K-Nearest Neighbour and Decision Tree Regression and performs competitively with state-of-the-art ensemble regression methods Random Forests and Gradient Boosting.","PeriodicalId":203136,"journal":{"name":"Proceedings of the 2020 Genetic and Evolutionary Computation Conference","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126004771","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: 5

Just-in-time batch scheduling subject to batch size 受批量大小影响的准时批调度

Proceedings of the 2020 Genetic and Evolutionary Computation Conference Pub Date : 2020-06-25 DOI: 10.1145/3377930.3390207

S. Polyakovskiy, D. Thiruvady, R. M’Hallah

{"title":"Just-in-time batch scheduling subject to batch size","authors":"S. Polyakovskiy, D. Thiruvady, R. M’Hallah","doi":"10.1145/3377930.3390207","DOIUrl":"https://doi.org/10.1145/3377930.3390207","url":null,"abstract":"This paper considers single-machine just-in-time scheduling of jobs that may be grouped into batches subject to a constraint on batches' weights. A job has a weight, due date, and earliness and tardiness penalties per unit time. A batch's processing time is determined by its jobs. Each job inherits its batch's completion time. The objective is to minimize the weighted sum of earliness and tardiness penalties of all jobs. This problem is challenging: Jobs-to-batch assignment changes the batch's processing time; thus, affects the structure of the entire solution and most importantly of its cost components. This problem is an excellent benchmark for testing linkage learning techniques, which exploit a problem's structure. We propose a matheuristic LTGA, which integrates evolutionary algorithms with mixed-integer programming (MIP). It builds a linkage tree that extracts the dependency among decision variables of MIP solutions. We compare its performance to a state of the art matheuristic CMSA, which combines the learning component of an ant colony system (ACS) with MIP within a construct, solve, merge, and adapt framework. It uses knowledge extracted from ACS' ants to construct a restricted MIP, solves it efficiently, and feeds it back to ACS. Computational tests indicate that LTGA outperforms MIP solvers and CMSA.","PeriodicalId":203136,"journal":{"name":"Proceedings of the 2020 Genetic and Evolutionary Computation Conference","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130091570","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: 8