Article
Computer Science, Software Engineering
Aitor Arrieta, Pablo Valle, Joseba A. Agirre, Goiuria Sagardui
Summary: This article proposes seeding strategies for the test case selection problem, which help to improve the performance of multi-objective search algorithms and find better solutions.
ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY
(2023)
Article
Management
Vladimir Korotkov, Desheng Wu
Summary: Risk assessment and selection of project portfolios are conducted under uncertainty, using historical data that can be adjusted in the future. By evaluating the robustness of portfolios with an accuracy function, investors can better assess market situations and make more rational decisions. Based on global risk assessment, the accuracy function is used to improve investment portfolios in projects participating in the Belt and Road initiative.
OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE
(2021)
Article
Green & Sustainable Science & Technology
Gonzalo E. Alvarez
Summary: A new multi-objective model is proposed to address cleaner production investments, considering the perspectives of various stakeholders. Applied to Argentina's energy system, results show that a mix of renewable and non-renewable technologies provides the most favorable solutions for all parties involved.
ENVIRONMENTAL PROGRESS & SUSTAINABLE ENERGY
(2022)
Article
Computer Science, Information Systems
Pilsu Jung, Sungwon Kang, Jihyun Lee
Summary: The paper presents a method to avoid redundant equivalent test executions in software product line testing, which can be applied to the first version and subsequent versions of a product family for regression testing. Additionally, a process for applying the method to software product line testing is proposed to enhance practical usability.
Article
Computer Science, Information Systems
Israr Ghani, Wan M. N. Wan-Kadir, Adila Firdaus Arbain, Noraini Ibrahim
Summary: Regression testing is an important but expensive activity in software development. Our proposed algorithm improves test case selection efficiency by reducing execution time and redundancy, and focuses on detecting modifications in test cases. The evaluation results show that our technique significantly reduces the inclusive testing time compared to a progressive approach in regression testing.
CMC-COMPUTERS MATERIALS & CONTINUA
(2022)
Article
Computer Science, Interdisciplinary Applications
Ioannis Gkioulekas, Lazaros G. Papageorgiou
Summary: This work presents a tree regression algorithm that optimally splits nodes into subsets using an optimization model and assesses partition quality with a statistical test. It also explores splitting nodes using multivariate decision rules to improve performance and computational efficiency. Introducing a novel mathematical model that selects an optimal set of variables for splitting on each node enhances the computational performance of the algorithm.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Davoud Mougouei, David M. W. Powers
Summary: In software development, Requirement Selection is a critical activity to find an optimal subset of software requirements with the highest value for a given budget. Value Dependencies among requirements have not been considered in existing methods, leading to user dissatisfaction and loss of value. We propose Dependency-Aware Requirements Selection (DARS) as an expert system that explicitly accounts for these dependencies, reducing the risk of value loss and proving scalability to large requirement sets.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Operations Research & Management Science
Dennis Adelhuette, Christian Biefel, Martina Kuchlbauer, Jan Rolfes
Summary: This paper introduces the application of Pareto efficiency to robust linear programming and generalizes this concept to robust optimization problems in Euclidean spaces with uncertainty. Additionally, it demonstrates the value of this approach through exemplary cases in the field of robust semidefinite programming. Furthermore, the paper modifies a famous algorithm to improve the approximation guarantee in non-worst-case scenarios for the robust max-cut problem.
OPTIMIZATION LETTERS
(2023)
Article
Management
Peter Biro, Jens Gudmundsson
Summary: This paper discusses the allocation of objects to agents, encoding welfare judgments as edge weights in an acceptability graph, introducing a constrained welfare-maximizing solution, and briefly discussing incentives for reporting preferences truthfully.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2021)
Article
Computer Science, Information Systems
Jinlong Zhou, Juan Zou, Shengxiang Yang, Jinhua Zheng, Dunwei Gong, Tingrui Pei
Summary: This paper proposes niche-based and angle-based selection strategies for many objective evolutionary optimization, which have been shown to be competitive and scalable to handle constrained many-objective optimization problems in experimental studies.
INFORMATION SCIENCES
(2021)
Article
Meteorology & Atmospheric Sciences
Tanja Zerenner, Victor Venema, Petra Friederichs, Clemens Simmer
Summary: Symbolic regression is used to estimate daily precipitation amounts in the Alpine region, generating a set of downscaling models with different achievable trade-offs between low RMSE and consistency in distribution. Deterministic downscaling models perform better with low RMSE, while stochastic models slightly outperform deterministic models in terms of IQD for the majority of cases. No approach is uniquely superior, with stochastic models providing useful distribution estimates capturing stochastic uncertainty similar to or slightly better than GLM-based downscaling.
INTERNATIONAL JOURNAL OF CLIMATOLOGY
(2021)
Article
Computer Science, Artificial Intelligence
Juan Zou, Jing Liu, Jinhua Zheng, Shengxiang Yang
Summary: This paper proposes a multi-objective optimization algorithm based on staged coordination selection, consisting of convergence and diversity stages. The algorithm aims to balance convergence and diversity in evolutionary algorithms, showing improved performance compared to existing algorithms on various benchmark instances.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
Article
Computer Science, Hardware & Architecture
Lilly Raamesh, S. Jothi, S. Radhika
Summary: This paper proposes a model based on Side-blotched lizard optimized AdaBoost Convolutional Neural Network (SBLA-AdaBoost CNN) to minimize test case execution cost and improve efficiency. The model is evaluated using the Defects4J dataset and shows high precision and recall scores, as well as reduced resource utilization and time consumption. This method can be applied during the initial stages of software testing.
JOURNAL OF SUPERCOMPUTING
(2022)
Article
Management
Sigrun Andradottir, Judy S. Lee
Summary: This study addresses the ranking and selection problem with multiple objectives by proposing three different formulations and verifying the effectiveness and efficiency of the algorithms through numerical experiments. The appropriate choices of parameter values and the guarantee of correct selection probability in a finite amount of time are demonstrated in each scenario.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2021)
Article
Computer Science, Software Engineering
Christian Birchler, Sajad Khatiri, Bill Bosshard, Alessio Gambi, Sebastiano Panichella
Summary: Simulation platforms are efficient and safe for testing emerging Cyber-Physical Systems like self-driving cars. However, thoroughly testing self-driving cars in simulated environments is challenging due to the large number of test cases. In this paper, we propose an approach called SDC-Scissor that uses machine learning to skip unnecessary test cases and improve cost-effectiveness. Evaluation results show that SDC-Scissor outperforms baseline strategies and can be applied in industrial settings.
EMPIRICAL SOFTWARE ENGINEERING
(2023)
Article
Biochemistry & Molecular Biology
Kristine Bohmann, Siavash Mirarab, Vineet Bafna, M. Thomas P. Gilbert
Article
Biochemistry & Molecular Biology
Uyen Mai, Siavash Mirarab
Summary: This paper introduces a new dating method wLogDate, which formulates dating as a nonconvex optimization problem, minimizing the variance of log-transformed rate multipliers across the tree. The method is shown to be more accurate and robust to various model assumptions than alternatives on simulated and real data.
MOLECULAR BIOLOGY AND EVOLUTION
(2021)
Correction
Biochemistry & Molecular Biology
Chao Zhang, Celine Scornavacca, Erin K. Molloy, Siavash Mirarab
MOLECULAR BIOLOGY AND EVOLUTION
(2021)
Article
Ecology
Chao Zhang, Yiming Zhao, Edward L. Braun, Siavash Mirarab
Summary: The text discusses the issue of erroneous data in sequence datasets and the need for automatic error detection methods as datasets grow larger. It introduces the TAPER method, which detects errors in species-specific stretches of sequence alignments to improve accuracy in downstream analyses.
METHODS IN ECOLOGY AND EVOLUTION
(2021)
Article
Biochemistry & Molecular Biology
Metin Balaban, Yueyu Jiang, Daniel Roush, Qiyun Zhu, Siavash Mirarab
Summary: This study introduces a distance-based phylogenetic placement method called APPLES-2, which is more accurate and scalable than existing methods. Through validation using a large dataset, it is shown that 97% of query genomes can be accurately placed within three branches of the optimal position in the species tree using 50 marker genes.
MOLECULAR ECOLOGY RESOURCES
(2022)
Article
Biochemical Research Methods
Shahab Sarmashghi, Metin Balaban, Eleonora Rachtman, Behrouz Touri, Siavash Mirarab, Vineet Bafna
Summary: The cost of genome sequencing is dropping faster than genome assembly and completion. The use of lightly sampled genomes and k-mers has advantages in identifying and phylogenetically placing eukaryotic species. A novel constrained optimization method can provide reliable estimates of genome length and repeat content.
PLOS COMPUTATIONAL BIOLOGY
(2021)
Article
Biochemical Research Methods
Maryam Rabiee, Siavash Mirarab
Summary: The paper introduces a scalable likelihood-based approach called quartet co-estimation for co-estimation under the multi-species coalescent model. By independently inferring gene tree distributions and computing species tree topology and branch length, the method updates gene tree posterior probabilities based on the resulting species tree. Experimental results on simulations and a biological dataset demonstrate better accuracy compared to traditional methods.
Article
Biology
Navid Bin Hasan, Metin Balaban, Avijit Biswas, Md Shamsuzzoha Bayzid, Siavash Mirarab
Summary: Phylogenetic identification of unknown sequences through tree placement is commonly used in ecological studies. This article addresses the issue of uncertainty in placements obtained from incomplete and noisy data. Nonparametric bootstrapping is found to be the most accurate method for measuring support, and an efficient linear algebraic formulation for bootstrapping is presented. The article also compares the accuracy of maximum likelihood support values and distance-based methods in different applications and datasets.
Article
Biochemical Research Methods
Chao Zhang, Siavash Mirarab
Summary: ASTRAL-Pro 2 is a more efficient version of ASTRAL-Pro, which enhances scalability while maintaining accuracy by adopting a placement-based optimization algorithm.
Article
Biochemistry & Molecular Biology
Chao Zhang, Siavash Mirarab
Summary: This paper introduces a threshold-free weighting scheme for quartet-based species tree inference, which improves the utility of summary methods and reduces incongruence with gene concatenation.
MOLECULAR BIOLOGY AND EVOLUTION
(2022)
Article
Biology
Yueyu Jiang, Puoya Tabaghi, Siavash Mirarab
Summary: This paper demonstrates how the conventional Euclidean deep learning methods in phylogenetics can benefit from using hyperbolic geometry. The results show that hyperbolic embeddings have lower distance errors and can be used to update species trees.
Article
Biochemical Research Methods
Yasamin Tabatabaee, Chao Zhang, Tandy Warnow, Siavash Mirarab
Summary: This article introduces a new method, CASTLES, for estimating branch lengths on the species tree using expected values of gene tree branch lengths. The method improves on prior methods in terms of both speed and accuracy.
Article
Biotechnology & Applied Microbiology
Metin Balaban, Yueyu Jiang, Qiyun Zhu, Daniel McDonald, Rob Knight, Siavash Mirarab
Summary: Large, updatable phylogenetic trees are constructed using a divide-and-conquer strategy called uDance. This method enables high accuracy and scalability in inferring genome-wide evolutionary relationships by refining different parts of the tree independently. With uDance, a species tree of around 200,000 genomes was successfully inferred using 387 marker genes, representing 42.5 billion amino acid residues.
NATURE BIOTECHNOLOGY
(2023)
Proceedings Paper
Biotechnology & Applied Microbiology
Krister M. Swenson, Afif Elghraoui, Faramarz Valafar, Siavash Mirarab, Mathias Weller
Summary: This article introduces a test method to measure the hierarchical relationship between two sets of homology relationships provided by different software. The test can be used to check the feasibility of agglomerative syntenic block software and provide a mapping reference for downstream analysis. The research finds that it is rare for two collections of homology relationships to be perfectly hierarchically related, so an optimization problem is proposed to measure the distance between them, and a heuristic solution is given.
COMPARATIVE GENOMICS (RECOMB-CG 2022)
(2022)
Proceedings Paper
Biotechnology & Applied Microbiology
Navid Bin Hasan, Avijit Biswas, Metin Balaban, Siavash Mirarab, Md. Shamsuzzoha Bayzid
Summary: Placing a new sequence onto an existing phylogenetic tree is important for various downstream applications. Existing methods often ignore the issue of uncertainty, but we have successfully estimated the distribution of placements using a distance-based approach and found that non-parametric bootstrapping is more accurate in estimating uncertainty.
COMPARATIVE GENOMICS (RECOMB-CG 2022)
(2022)