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Application of Search Algorithms for Model Based Regression Testing

Sidra Noureen, Sohail Asghar
2014 Research Journal of Applied Sciences Engineering and Technology  
The motivation of this study is to design a multi objective genetic algorithm based test case selection technique which can select the most appropriate subset of test cases.  ...  With the help of Model Based Testing (MBT), it is possible to automatically generate test cases.  ...  RESEARCH METHODOLOGY In the previous section, evolutionary and search based algorithms are discussed for model based testing.  ... 
doi:10.19026/rjaset.7.630 fatcat:xyojplgxxjelthzddquzs3ihwy

A Multi-Objective Evolutionary Algorithm based Feature Selection for Intrusion Detection

2017 Iraqi Journal of Science  
In this paper, multi-objective evolutionary algorithm with decomposition (MOEA/D) and MOEA/D with the injection of a proposed local search operator are adopted to solve the Multi-objective optimization  ...  The performance of the proposed models is evaluated against two baseline models feature vitality based reduction method (FVBRM) and .  ...  each fold in NSL-KDD testing dataset were conducted in order to assess the effectiveness of the proposed multi-objective optimization based models and for intrusion detection.  ... 
doi:10.24996/ijs.2017.58.1c.16 fatcat:vikc3sa4fffjnhwfiomryxabou

Comparative analysis of classical multi-objective evolutionary algorithms and seeding strategies for pairwise testing of Software Product Lines

Roberto E. Lopez-Herrejon, Javier Ferrer, Francisco Chicano, Alexander Egyed, Enrique Alba
2014 2014 IEEE Congress on Evolutionary Computation (CEC)  
In contrast and to address this need, our work is the first to propose a classical multi-objective formalisation where both objectives are equally important.  ...  Most approaches for SPL pairwise testing have focused on achieving full coverage of all pairwise feature combinations with the minimum number of products to test.  ...  when a particular multi-objective algorithm performs better based, for example, on structural metrics of the feature models [60] .  ... 
doi:10.1109/cec.2014.6900473 dblp:conf/cec/Lopez-HerrejonFCEA14 fatcat:y4rmxspsxvfxre7tpggndgitte

A Two stage Adaptive Knowledge Transfer Evolutionary Multi-tasking Based on Population Distribution for Multi/Many-Objective Optimization [article]

Zhengping Liang, Weiqi Liang, Xiuju Xu, Ling Liu, Zexuan Zhu
2020 arXiv   pre-print
EMT-PD can accelerate and improve the convergence performance of tasks based on the knowledge extracted from the probability model that reflects the search trend of the whole population.  ...  To further investigate the effectiveness of EMT-PD on many-objective optimization problems, a multi-tasking many-objective test suite is also designed in this paper.  ...  EMT-PD is tested on multi-tasking multi/many-objective optimization test suites.  ... 
arXiv:2001.00810v3 fatcat:fdy75uapdfbmlf7mtwunxf2opu

Inferring Test Models from Kate's Bug Reports Using Multi-objective Search [chapter]

Yuanyuan Zhang, Mark Harman, Yue Jia, Federica Sarro
2015 Lecture Notes in Computer Science  
Our search-based model inference approach considers three objectives: 1) to reduce the number of invalid user events generated (over approximation), 2) to reduce the number of unrecognised user events  ...  Models inferred from system execution logs can be used to test general system behaviour. In this paper, we infer test models from user bug reports that are written in the natural language.  ...  Acknowledgements: We wish to express our gratitude to Paolo Tonella for his helpful suggestion and the search-based FSM tools provided.  ... 
doi:10.1007/978-3-319-22183-0_27 fatcat:pjx4wcndcjfnbjzt56a7o36vs4

Effective, Efficient and Robust Neural Architecture Search [article]

Zhixiong Yue, Baijiong Lin, Xiaonan Huang, Yu Zhang
2020 arXiv   pre-print
To solve the proposed objective function, we integrate the multiple-gradient descent algorithm, a widely studied gradient-based multi-objective optimization algorithm, with the bi-level optimization.  ...  The objective function of the proposed E2RNAS method is formulated as a bi-level multi-objective optimization problem with the upper-level problem as a multi-objective optimization problem, which is different  ...  FBNet [31] also considers both the accuracy and model latency when searching the architecture via a gradient-based method to solve the corresponding multi-objective problem.  ... 
arXiv:2011.09820v1 fatcat:2dc3ccqivjc63pbucg5fj7obyi

Evolutionary Algorithms Based on Decomposition and Indicator Functions: State-of-the-art Survey

Wali Khan, Abdellah Salhi, Muhammad Asif, Muhammad Sulaiman, Rashida Adeeb, Abdulmohsen Algarni
2016 International Journal of Advanced Computer Science and Applications  
Multi-Objective Evolutionary Algorithms (MOEAs) play a dominant role in solving problems with multiple conflicting objective functions.  ...  of Fuzzy dominance concepts and many other advanced techniques for dealing with diverse optimization and search problems  ...  In [29] , a generalized methodology for preference-directed hypervolume-based multi objective search called W-HypE is developed.  ... 
doi:10.14569/ijacsa.2016.070274 fatcat:3oleqyfntzdz5hwkd3f5df56qi

Compositional Attention: Disentangling Search and Retrieval [article]

Sarthak Mittal, Sharath Chandra Raparthy, Irina Rish, Yoshua Bengio, Guillaume Lajoie
2021 arXiv   pre-print
Multi-head, key-value attention is the backbone of the widely successful Transformer model and its variants.  ...  Our proposed mechanism generalizes multi-head attention, allows independent scaling of search and retrieval, and can easily be implemented in lieu of standard attention heads in any network architecture  ...  Much like other cutting edge ML methods, careful considerations of potential negative impacts on society should be considered before deploying models for applied uses.  ... 
arXiv:2110.09419v1 fatcat:ikwtvmxgp5bozmgmw3ghcnt7lm

Model-Based Multi-objective Optimization: Taxonomy, Multi-Point Proposal, Toolbox and Benchmark [chapter]

Daniel Horn, Tobias Wagner, Dirk Biermann, Claus Weihs, Bernd Bischl
2015 Lecture Notes in Computer Science  
Within the last 10 years, many model-based multi-objective optimization algorithms have been proposed. In this paper, a taxonomy of these algorithms is derived.  ...  In particular for the classic ParEGO algorithm, significant improvements are obtained.  ...  EI Multi point, space-filling selection Total budget MOEA using Surrogates [19] Other DOE (Sobol) Models for each objective Multi-objective optimization of model prediction Multi  ... 
doi:10.1007/978-3-319-15934-8_5 fatcat:tspwa3upefbn3edqn5mjprc5h4

Efficient Multi-Objective Optimization through Population-based Parallel Surrogate Search [article]

Taimoor Akhtar, Christine A. Shoemaker
2019 arXiv   pre-print
Multi-Objective Optimization (MOO) is very difficult for expensive functions because most current MOO methods rely on a large number of function evaluations to get an accurate solution.  ...  of N points so that the expensive objectives for all points are simultaneously evaluated on N processors in each iteration.  ...  The support at NUS was from the Singapore National Research Foundation, Prime Minister's Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme (E2S2  ... 
arXiv:1903.02167v1 fatcat:xnvwghchq5hujlbijz3nnd66pm

PID2018 Benchmark Challenge:Multi-Objective Stochastic Optimization Algorithm [article]

Abdullah Ates, Jie Yuan, Sina Dehghan, Yang Zhao, Celaleddin Yeroglu, YangQuan Chen
2018 arXiv   pre-print
This paper presents a multi-objective stochastic optimization method for tuning of the controller parameters of Refrigeration Systems based on Vapour Compression.  ...  Stochastic Multi Parameter Divergence Optimization (SMDO) algorithm is modified for minimization of the Multi Objective function for optimization process.  ...  Furthermore, multi objective functions can be based on the error functions that are MSE, ITAE, ISE and IAE. In the literature many studies deal with multi objective optimization.  ... 
arXiv:1806.00958v1 fatcat:fydgg53a6ffnldzgzzfnldkat4

Parallel multi-objective optimization using Master-Slave model on heterogeneous resources

Sanaz Mostaghim, Jurgen Branke, Andrew Lewis, Hartmut Schmeck
2008 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)  
For this we propose a hybrid method using Multi-objective Particle Swarm optimization and Binary search methods.  ...  In this paper, we study parallelization of multiobjective optimization algorithms on a set of hetergeneous resources based on the Master-Slave model.  ...  Here, we use some standard test functions from the multi-objective optimization literature.  ... 
doi:10.1109/cec.2008.4631060 dblp:conf/cec/MostaghimBLS08 fatcat:mwdwujmamfedpmmlmmmwx2y7oy

Multi-Objective Optimal Design of a Building Envelope and Structural System Using Cyber-Physical Modeling in a Wind Tunnel

Michael L. Whiteman, Pedro L. Fernández-Cabán, Brian M. Phillips, Forrest J. Masters, Jennifer A. Bridge, Justin R. Davis
2018 Frontiers in Built Environment  
To explore a CPS approach to multi-objective optimization, a low-rise building with a parapet wall of variable height is considered.  ...  Both the components and cladding (C&C) and the main wind force resisting system (MWFRS) are considered through multi-objective optimization.  ...  The paper also considers a stochastic search algorithm (PSO) that is better suited for multi-variate and multi-objective optimization problems with no requirements on the continuity or form of the objective  ... 
doi:10.3389/fbuil.2018.00013 fatcat:zgtbofv7vbbx7gj2itviepqjaq

Parallel Distributed Two-Level Evolutionary Multiobjective Methodology for Granularity Learning and Membership Functions Tuning in Linguistic Fuzzy Systems

Miguel A. De Vega, Juan M. Bardallo, Francisco A. Márquez, Antonio Peregrín
2009 2009 Ninth International Conference on Intelligent Systems Design and Applications  
The presented methodology employs a high level main evolutionary multi-objective heuristic searching the number of labels, and some distributed low level ones, also evolutionary, tuning the membership  ...  with different trade-offs between accuracy and complexity, through the use of a two-level evolutionary multi-objective algorithm.  ...  We propose to use a high level single search based on a multi-objective algorithm to find a set of optimal granularity combinations of the variables using accuracy and complexity as objectives.  ... 
doi:10.1109/isda.2009.225 dblp:conf/isda/VegaBMP09 fatcat:5qfakvjwojajvnbs2vc2oyfw7y

A Multi-objective Approach for the Calibration of Microscopic Traffic Flow Simulation Models

Carlos Cobos, Alexander Paz, Julio Luna, Cristian Erazo, Martha Mendoza
2020 IEEE Access  
This study proposes an adaptation and advanced implementation of the Multi-Objective Global-Best Harmony Search (MOGBHS) algorithm for calibrating microscopic trafficflow simulation models.  ...  Three traffic flow models of different dimensionality and complexity were used to test the performance of seventeen metaheuristics for solving the calibration problem.  ...  Other available multi-objective algorithms that were not tested but have potential for solving the calibration problem include, among others, MOEA/D (a decomposition-based evolutionary multi-objective  ... 
doi:10.1109/access.2020.2999081 fatcat:wehtmim24rafvicgm6rv7eqwfu
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