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Fragile Watermarking of 3D Models Using genetic algorithms

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Journal of Electronic Science and Technology 2010年第3期8卷 244-250页

作者： Mukesh Motwani Rakhi Motwani Frederick Harris University of Nevada

This paper describes a novel algorithm for fragile watermarking of 3D models. Fragile watermarking requires detection of even minute intentional changes to the 3D model along with the location of the change. This poses a challenge since inserting random amount of watermark in all the vertices of the model would generally introduce perceptible distortion. The proposed algorithm overcomes this challenge by using genetic algorithm to modify every vertex location in the model so that there is no perceptible distortion. Various experimental results are used to justify the choice of the genetic algorithm design parameters. Experimental results also indicate that the proposed algorithm can accurately detect location of any mesh modification.

关键词： 3D mesh models fragile water- marking genetic algorithms SNR.

Smart Contract Fuzzing Based on Taint Analysis and genetic algorithms

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Journal of Quantum Computing 2020年第1期2卷 11-24页

作者： Zaoyu Wei Jiaqi Wang Xueqi Shen Qun Luo School of Cyberspace Security Beijing University of Posts and TelecommunicationsBeijing100876China College of New Media Beijing Institute of Graphic CommunicationBeijing102600China

Smart contract has greatly improved the services and capabilities of blockchain,but it has become the weakest link of blockchain security because of its code ***,efficient vulnerability detection of smart contract is the key to ensure the security of blockchain *** to Ethereum smart contract,the study solves the problems of redundant input and low coverage in the smart contract *** this paper,a taint analysis method based on EVM is proposed to reduce the invalid input,a dangerous operation database is designed to identify the dangerous input,and genetic algorithm is used to optimize the code coverage of the input,which construct the fuzzing framework for smart contract ***,by comparing Oyente and ContractFuzzer,the performance and efficiency of the framework are proved.

关键词： Smart contract fuzzing taint analysis genetic algorithms

Job-shop Scheduling with Multi-objectives Based on genetic algorithms

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Journal of Donghua University(English Edition) 2003年第3期20卷 57-62页

作者：周亚勤李蓓智陈革 College of Mechanical Engineering Donghua University Shanghai 200051

The technology of production planning and scheduling is one of the critical technologies that decide whether the automated manufacturing systems can get the expected economy. Job shop scheduling belongs to the special class of NP-hard problems. Most of the algorithms used to optimize this class of problems have an exponential time; that is, the computation time increases exponentially with problem size. In scheduling study, makespan is often considered as the main objective. In this paper, makespan, the due date request of the key jobs, the availability of the key machine, the average wait-time of the jobs, and the similarities between the jobs and so on are taken into account based on the application of mechanical engineering. The job shop scheduling problem with multi-objectives is analyzed and studied by using genetic algorithms based on the mechanics of genetics and natural selection. In this research, the tactics of the coding and decoding and the design of the genetic operators, along with the description of the mathematic model of the multi-objective functions, are presented. Finally an illu-strative example is given to testify the validity of this algorithm.

关键词： job shop scheduling multi-objective optimization genetic algorithms

Research on the fully fuzzy time-cost trade-off based on genetic algorithms

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Journal of Marine Science and Application 2005年第3期4卷 18-23页

作者： JIN Chao-guang JI Zhuo-shang LIN Yan ZHAO Yuan-min HUANG Zhen-dong Ship CAD Engineering Center Dalian University of Technology Dalian 116025 China Dalian Shipyard Co. Ltd. Dalian 116005 China

It is very difficult to estimate exact values of time and cost of an activity in project scheduling process because many uncertain factors, such as weather, productivity level, human factors etc. , dynamically affect them during project implementation process. A GAs-based fully fuzzy optimal time-cost trade-off model is presented based on fuzzy sets and genetic algorithms (GAs). In tihs model all parameters and variables are characteristics by fuzzy numbers. And then GAs is adopted to search for the optimal solution to this model. The method solves the time-cost trade-off problems under an uncertain environment and is proved practicable through a giving example in ship building scheduling.

关键词： fuzzy sets ranking fuzzy number genetic algorithms time-cost trade-off

Solving traveling salesman problems by genetic algorithms

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Progress in Natural Science:Materials International 2003年第2期 57-63页

作者： LEE Heow Pueh,LIM Siak Piang,LEE Kwok Hong Institute of High Performance Computing Singapore 117528 Singapore Department of Mechanical Engineering National University of Singapore Singapore 119260 Singapore Department of Mechanical Engineering National University of Singapore Singapore 119260 Singapore

Abstract The gene section ordering on solving traveling salesman problems is analyzed by numerical experiments. Some improvedcrossover operations are presented. Several combinations of genetic operations are examined and the functions of these operations are ana-lyzed. The essentiality of the ordering of the gene section and the significance of the evolutionary inversion operation are discussed. Someresults and conclusions are obtained and given, which provide useful information for the implementation of the genetic operations for solv-ing the traveling salesman problem.

关键词： genetic algorithms traveling salesman problem gene section evolutionary inversion genetic operations.

Underwater vehicle sonar self-noise prediction based on genetic algorithms and neural network

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Journal of Marine Science and Application 2006年第2期5卷 36-41页

作者： WU Xiao-guang SHI Zhong-kun College of Traffic Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China China Ship Development and Design Center Wuhan 430064 China

The factors that influence underwater vehicle sonar self-noise are analyzed, and genetic algorithms and a back propagation (BP) neural network are combined to predict underwater vehicle sonar self-noise. The experimental results demonstrate that underwater vehicle sonar self-noise can be predicted accurately by a GA-BP neural network that is based on actual underwater vehicle sonar data.

关键词： sonar self-noise back propagation (BP) neural network genetic algorithms

The schema deceptiveness and deceptive problems of genetic algorithms

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Science in China(Series F) 2001年第5期44卷 342-350页

作者：李敏强寇纪淞 Institute of Systems Engineering Tianjin University Tianjin China

genetic algorithms (GA) are a new type of global optimization methodology based on na-ture selection and heredity, and its power comes from the evolution process of the population of feasi-ble solutions by using simple genetic operators. The past two decades saw a lot of successful industrial cases of GA application, and also revealed the urgency of practical theoretic guidance. This paper sets focus on the evolution dynamics of GA based on schema theorem and building block hypothesis (Schema Theory), which we thought would form the basis of profound theory of GA. The deceptive-ness of GA in solving multi-modal optimization problems encoded on {0,1} was probed in detail. First, a series of new concepts are defined mathematically as the schemata containment, schemata compe-tence. Then, we defined the schema deceptiveness and GA deceptive problems based on primary schemata competence, including fully deceptive problem, consistently deceptive problem, chronically deceptive problem, and fundamentally deceptive problem. Meanwhile, some novel propositions are formed on the basis of primary schemata competence. Finally, we use the trap function, a kind of bit unitation function, and a NiH function (needle-in-a-haystack) newly designed by the authors, to dis-play the affections of schema deceptiveness on the searching behavior of GA.

关键词： genetic algorithms schema competition schema deceptiveness GA deceptive problems.

Granularity of Knowledge Computed by genetic algorithms Based on Rough Sets Theory

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南昌工程学院学报 2006年第2期25卷 97-101,121页

作者： Wenyuan Yang Xiaoping Ye Yong Tang Pingping Wei Dep. of Computer Engineering Zhangzhou Institute of Technology Zhangzhou 363000China Dep. of Computer Science Sun Yat-Sen University Guangzhou 510275China Dep. of Math and Computer Science Guizhou Institute of Education Guiyang 550003China

Rough set philosophy hinges on the granularity of data, which is used to build all its basic concepts, like approximations, dependencies, reduction etc. genetic algorithms provides a general frame to optimize problem solution of complex system without depending on the domain of *** is robust to many kinds of *** paper combines genetic algorithms and rough sets theory to compute granular of knowledge through an example of information table. The combination enable us to compute granular of knowledge *** is also useful for computer auto-computing and information processing.

关键词： granularity of knowledge genetic algorithms Pawlak Model Rough Set Theory information table

Automatic Identification of Tomato Maturation Using Multilayer Feed Forward Neural Network with genetic algorithms (GA)

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Journal of Northeast Agricultural University(English Edition) 2004年第2期11卷 179-183页

作者： FANG Jun-long ZHANG Chang-li WANG Shu-wen EngineeringCollege NortheastAgriculturalUniversityHarbinHeilongjiang150030PRC

We set up computer vision system for tomato images. By using this system, the RGB value of tomato image was converted into HIS value whose H was used to acquire the color character of the surface of tomato. To use multilayer feed forward neural network with GA can finish automatic identification of tomato maturation. The results of experiment showed that the accuracy was up to 94%.

关键词： tomato maturation computer vision artificial neural network genetic algorithms