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Diagnostic Test Generation for Statistical Bug Localization Using evolutionary computation 17

17th European Conference on Applications of evolutionary computation (EvpApplications)

作者： Gaudesi, Marco Jenihhin, Maksim Raik, Jaan Sanchez, Ernesto Squillero, Giovanni Tihhomirov, Valentin Ubar, Raimund Politecn Torino Turin Italy Tallinn Univ Technol EE-19086 Tallinn Estonia

ISBN: (纸本)9783662455234;9783662455227

Verification is increasingly becoming a bottleneck in the process of designing electronic circuits. While there exists several verification tools that assist in detecting occurrences of design errors, or bugs, there is a lack of solutions for accurately pin-pointing the root causes of these errors. Statistical bug localization has proven to be an approach that scales up to large designs and is widely utilized both in debugging hardware and software. However, the accuracy of localization is highly dependent on the quality of the stimuli. In this paper we formulate diagnostic test set generation as a task for an evolutionary algorithm, and propose dedicated fitness functions that closely correlate with the bug localization capabilities. We perform experiments on the register-transfer level design of the Plasma microprocessor coupling an evolutionary test-pattern generator and a simulator for fitness evaluation. As a result, the diagnostic resolution of the tests is significantly improved.

关键词： Diagnostic test pattern generation Design error localization evolutionary computation MicroGP ZamiaCAD

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TN-RSI: Trend-Normalized RSI indicator for Stock Trading Systems with evolutionary computation

TN-RSI: Trend-Normalized RSI indicator for Stock Trading Sys...

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Conference on Conquering Complexity - Challenges and Opportunities

作者： Sahin, Ugur Ozbayoglu, A. Murat TOBB Univ Econ & Technol Dept Comp Engn TR-06560 Ankara Turkey

RSI is a commonly used indicator preferred by stock traders. However, even though it works well when the market is trendless, during bull or bear market conditions (when there is a clear trend) its performance degrades. In this study, we developed a trading model using a modified RSI using trend-removed stock data. The model has several parameters including, the trend detection period, RSI buy-sell trigger levels and periods. These parameters are optimized using genetic algorithms;then the trading performance is compared against B&H and standard RSI indicator usage. 9 different ETFs are selected for evaluating trading performance. The results indicate there is a performance improvement both in profit and success rates using this new model. As future work, other indicators might be modelled in a similar fashion in order to see if it is possible to find one indicator that can work under any market condition. (C) 2014 The Authors. Published by Elsevier B.V.

关键词： stock market forecasting RSI genetic algorithms stock trading evolutionary computation trend detection

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Using evolutionary computation to Shed Light on the Effect of Scale and Complexity on Object-Oriented Software Design

Using Evolutionary Computation to Shed Light on the Effect o...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Simons, Christopher L. Smith, Jim Univ W England Dept Comp Sci & Creat Technol Bristol BS16 1QY Avon England

ISBN: (纸本)9781479938407

Early lifecycle software design is an intensely human activity in which design scale and complexity can place a high cognitive load on the software designer. Recently, the use of evolutionary search has been suggested to yield insights in the nature of software engineering problems generally, and so we have applied dynamic evolutionary computation using selfadaptive mutation to the object-oriented software design search space. Using three design problem instances of varying scale and complexity, initial investigations of the discrete search landscape reveal a redundancy in genotype-to-phenotype mapping enabling flexible and effective exploration. In further experiments, mutation probabilities and population diversity are observed to significantly increase in the face of increasing problem scale, but not for increasing complexity (in problems of the same scale). Based on these findings, we conclude that design problem scale rather than complexity has an effect on the software design process, emphasizing the role of decomposition as a design technique.

关键词： software design evolutionary computation

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Fitness function for evolutionary computation applied in dynamic object simulation and positioning

Fitness function for evolutionary computation applied in dyn...

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IEEE Symposium on computational Intelligence in Vehicles and Transportation Systems (CIVTS)

作者： Wozniak, Marcin Silesian Tech Univ Inst Math Ul Kaszubska 23 PL-44100 Gliwice Poland

ISBN: (纸本)9781479944972

In the paper an idea to apply evolutionary computation method with dedicated fitness function in dynamic system simulation and positioning is presented. Dedicated evolutionary system's efficiency in simulation, optimization and positioning of examined object is discussed. Presented experiments show common duty as well as extensive, overloading and dangerous situations at work. Research results are presented to discuss applied method.

关键词： evolutionary computation simulation dynamic object positioning dynamic object simulation fitness function Biological cells computational modeling Equations Mathematical model Suspensions Vehicle dynamics Vehicles evolutionary algorithm cytology Vehicle dynamics computational modeling vehicle Suspensions simulating Mathematical Model Patient Positioning

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EVOLVE - A Bridge between Probability, Set Oriented Numerics, and evolutionary computation V 1

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丛书名： Advances in Intelligent Systems and Computing

2014年

作者： Alexandru-Adrian Tantar, Emilia Tantar, Jian-Qiao Sun, Wei Zhang, Qian Ding, Oliver Schütze, Michael Emmerich, Pierrick Legrand, Pierre Del Moral, Carlos A. Coello Coello (eds.)

ISBN: (数字)9783319074948

ISBN: (纸本)9783319074931;9783319074948

This volume encloses research articles that were presented at the EVOLVE 2014 International Conference in Beijing, China, July 14, 2014. The book gathers contributions that emerged from the conference tracks, ranging from probability to set oriented numerics and evolutionary computation; all complemented by the bridging purpose of the conference, e.g. Complex Networks and Landscape Analysis, or by the more application oriented perspective. The novelty of the volume, when considering the EVOLVE series, comes from targeting also the practitioners view. This is supported by the Machine Learning Applied to Networks and Practical Aspects of evolutionary Algorithms tracks, providing surveys on new application areas, as in the networking area and useful insights in the development of evolutionary techniques, from a practitioners perspective. Complementary to these directions, the conference tracks supporting the volume, follow on the individual advancements of the subareas constituting the scope of the conference, through the computational Game Theory, Local Search and Optimization, Genetic Programming, evolutionary Multi-objective optimization tracks.

关键词： evolutionary computation Combinatorial optimization

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Many-objective evolutionary computation for optimization of separated-flow control using a DBD plasma actuator

Many-objective evolutionary computation for optimization of ...

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IEEE Congress on evolutionary computation (CEC)

作者： Watanabe, Takeshi Tatsukawa, Tomoaki Jaimes, Antonio Lopez Aono, Hikaru Nonomura, Taku Oyama, Akira Fujii, Kozo Japan Aerosp Explorat Agcy Inst Space & Astronaut Sci Chuo Ku Sagamihara Kanagawa 2525210 Japan

ISBN: (纸本)9781479914883

In this paper, an algorithm for many-objective evolutionary computation, which is based on the NSGA-II with the Chebyshev preference relation, is applied to multi-objective design optimization problem of dielectric barrier discharge plasma actuator (DBDPA). The present optimization problem has four design parameters and six objective functions. The main goal of the paper is to extract useful design guidelines to predict control flow behavior based on the DBDPA parameter values using the resulting approximation Pareto set obtained by the optimization.

关键词： Aerospace electronics Automotive components Chebyshev approximation Dielectrics evolutionary computation Optimization Plasmas

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Image Segmentation: A Survey of Methods Based on evolutionary computation

Image Segmentation: A Survey of Methods Based on Evolutionar...

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10th International Conference on Simulated Evolution and Learning (SEAL)

作者： Liang, Yuyu Zhang, Mengjie Browne, Will N. Victoria Univ Wellington Sch Engn & Comp Sci Wellington 6140 New Zealand

ISBN: (纸本)9783319135632;9783319135625

Image segmentation is mainly used as a preprocessing step in problems of image processing and computer vision. Its performance has a great influence on subsequent tasks. evolutionary computation (EC) techniques have been introduced to the area of image segmentation due to their high search capacity. However, there are rarely comprehensive surveys on EC based image segmentation methods, which can enable researchers to get a quick understanding of this area and compare the existing methods. Therefore, this paper provides an overview of EC based image segmentation methods, and discusses the remaining issues in this area. It is observed that among all EC techniques, four of them (genetic algorithms, genetic programming, differential equation and partial swarm optimization) are more frequently used and GAs are the most popular technique. It is noted that low generalization capacity and computational complexity are two common problems in EC techniques applied to image segmentation.

关键词： evolutionary Computer Vision Image Segmentation evolutionary computation Genetic Algorithms Genetic Programming

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Combining evolutionary computation and Algebraic Constructions to Find Cryptography-Relevant Boolean Functions

Combining Evolutionary Computation and Algebraic Constructio...

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13th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Picek, Stjepan Marchiori, Elena Batina, Lejla Jakobovic, Domagoj Radboud Univ Nijmegen Inst Comp & Informat Sci NL-6500 GL Nijmegen Netherlands Univ Zagreb Fac Elect Engn & Comp Zagreb 41000 Croatia

ISBN: (纸本)9783319107622;9783319107615

Boolean functions play a central role in security applications because they constitute one of the basic primitives for modern cryptographic services. In the last decades, research on Boolean functions has been boosted due to the importance of security in many diverse public systems relying on such technology. A main focus is to find Boolean functions with specific properties. An open problem in this context is to find a balanced Boolean function with an 8-bit input and nonlinearity 118. Theoretically, such a function has been shown to exist, but it has not been found yet. In this work we focus on specific classes of Boolean functions, and analyze the landscape of results obtained by integrating algebraic and evolutionary computation (EC) based approaches. Results indicate that combinations of these approaches give better results although not reaching 118 nonlinearity.

关键词： Boolean Functions Nonlinearity evolutionary computation Bent Functions Cryptographic Properties

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Machine Learning Based Modeling of Electrical Characteristics in Triangular Gate FinFETs for Low Power Electronics

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INTERNATIONAL JOURNAL OF NUMERICAL MODELLING-ELECTRONIC NETWORKS DEVICES AND FIELDS 2025年第2期38卷

作者： Hemalatha, M. Balamurugan, N. B. Suguna, M. Ayyanar, N. Thiagarajar Coll Engn Dept ECE Madurai India Thiagarajar Coll Engn Dept IT Madurai India

Modeling and optimization of devices play a critical role in the management of product quality and the advancement of technology within the industrial sector. With the advent of novel devices and the progression of technology, these devices exhibit a multitude of interrelated factors and demonstrate a nonlinear correlation. Triangular Gate (TG) FinFETs technology has emerged as a possible alternative for addressing the limitations of traditional planar transistors in present integrated circuits (ICs). This paper presents an effective data-driven Multiobjective Optimization (MOO) with evolutionary computation (EC) techniques. By using these techniques, TG FinFETs enables the automated identification of optimal design that balances the transistor speed, power, and variability. To assist in the design of TG FinFETs, this study integrated two popular MOO techniques such as PAL and NSGA-III. These algorithms effectively handle the complicated trade-offs between diverse objectives and allow for efficient and effective TG FinFETs design optimization.

关键词： device modeling evolutionary computation machine learning optimization TG FinFETs

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evolutionary Algorithms and Quantum Computing: Recent Advances, Opportunities, and Challenges

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IEEE ACCESS 2025年 13卷 16649-16670页

作者： Ur Rehman, Junaid Ulum, Muhammad Shohibul Shaffar, Abdurrahman Wachid Hakim, Amirul Adlil Mujirin, Zaid Abdullah, Zaid Al-Hraishawi, Hayder Chatzinotas, Symeon Shin, Hyundong King Fahd Univ Petr & Minerals Dept Elect Engn Dhahran 31261 Saudi Arabia Interdisciplinary Ctr Secur Reliabil & Trust SnT L-1855 Luxembourg City Luxembourg King Fahd Univ Petr & Minerals Interdisciplinary Res Ctr Intelligent Secure Syst Dhahran 31261 Saudi Arabia Kyung Hee Univ Dept Elect & Informat Convergence Engn Yongin 17104 Gyeonggi Do South Korea Univ S Florida Dept Elect Engn Tampa FL 33620 USA

Quantum computers have made significant progress in the last two decades showing great potential in tackling some of the most challenging problems in computing. This ongoing progress creates an opportunity to implement and evaluate quantum-inspired metaheuristics on real quantum devices, with the aim of uncovering potential computational advantages. Additionally, the practical constraints associated with current quantum computers have highlighted a critical need for classical heuristic methods to optimize the tunable parameters of quantum circuits. Nature-inspired metaheuristics have emerged as promising candidates for fulfilling this optimization role. In this paper, we discuss both of these potential directions at the intersection of evolutionary computing and quantum computing while surveying some of the most promising advancements in these directions. We start with the review of quantum-inspired metaheuristics and then explore implementations of some of these quantum-inspired algorithms on physical quantum devices, capitalizing on the progress in quantum computing technology. Furthermore, we investigate the role of nature-inspired metaheuristics in enhancing the performance of noisy intermediate-scale quantum computers by fine-tuning their parameters. Finally, we discuss some of the recent progress at the intersection of both computing frameworks to highlight the current status and potential of the currently available quantum computing hardware. Synergies between these two computing frameworks demonstrate the potential of a strongly symbiotic relation that can contribute to the simultaneous advancements in both of these computing paradigms.

关键词： Quantum computing evolutionary computation Qubit Quantum mechanics Metaheuristics Heuristic algorithms Hardware Terminology Computers Recommender systems evolutionary algorithms genetic algorithm quantum-inspired algorithms quantum computing

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