International Journal for Science and Emerging
|
|
- Sheena Cross
- 8 years ago
- Views:
Transcription
1 International Journal for Science and Emerging ISSN No. (Online): Technologies with Latest Trends 6(1): 1-6 (2013) ISSN No. (Print): Multi-Objective Genetic Algorithm for FPGA Testing Vinay Chopra* and Dr. Amardeep Singh** A.P. (Department of Computer Science)* DAV Institute of Engg & Tech. Jalandhar, India Professor (Department of Computer Science)** UCoE Punjabi University. Patiala, India (Received 10 March 2013 Accepted 14 March 2013) Abstract:This paper gives a concise introduction to Multi-Objective Genetic Algorithms and FPGAs and it reveals that how Automatic Test Pattern Generation method can be formulated in terms of CNF form which in turn used to generate test patterns using Multi-Objective Genetic Algorithm. By applying a Multi-Objective Genetic Algorithm on this CNF form, it has been observed from the experiments that as the problem size are increased by increasing number of variables and clauses, the fault coverage ratio increases. It also has been shown that Multiobjective Genetic Algorithm can be applied to variety of SAT instances of FPGA test pattern generation problem, which gives competitive results. Keywords: FPGAs, CNF, MOGA, DAG, SAT, ATPG, PODEM, FAN 1. Introduction to FPGA Testing FPGA testing or ATPG[1] is a major research topic which is used in semiconductor electrical testing in which the vectors or input patterns are required to check a device for faults that are automatically generated by a program. The input vector patterns are sequentially applied to the device under test and its corresponding response is compared with the expected response from a good circuit. If any error occurs it means that the circuit is faulty. The effectiveness of the ATPG is considered largely by the fault coverage achieved and the cost of conducting the test. Mainly three types of approaches have been recommended for sequential circuits The Topological Approach [2, 3], Symbolic Approach [4] and the Simulation based Approach [5, 6]. Automatic Test Pattern Generation (ATPG) consist of two distinct phases, firstly a creation of the test vector and secondly an application of the test vector. In the first phase, suitable models for the FPGA circuit are developed at gate or transistor level in a way so that the output responses of a faulty device for a given set of inputs will be different from those of good devices [7]. An efficient test vector is which make an efficient use of memory space, time taken during fault coverage and minimum set of test vectors must generated to detect all the important faults of a device [8]. The parameters that should be kept in mind during the design of test vector suit are the time needed to construct the minimal test set, size of the pattern generator( hardware/software system needed to properly stimulate the devices under test),size of the testing process itself, time needed to load the test patterns,external equipment etc. Automatic Test Pattern Generation (ATPG) methods that are mostly used include the D-Algorithm [9], the PODEM [10], and the FAN [11]. 2. Introduction to Multi-objective Genetic Algorithm Multi-objective optimization problems (MOPs) [12, 13] are common Optimization Problem. A general MOP includes a set of parameters (decision variables), a set of k objective functions, and a set of m constraints. Objective functions and constraints are functions of the decision variables. Multi-Objective Genetic Algorithms (GA) imitate the biological processes fundamental classic Darwinian evolution for finding solutions to optimization or classification
2 2 Chopra and Singh problems. The implementation is based on finding a population of candidate solutions (or chromosomes) which is evaluated using a fitness function and ranked. From the ranking, candidates are selected from which the next generation is created [14]. The fitness function is a measure of how well a candidate solves the problem. Implementations vary in the choice and practice of the selection method; i.e. the purpose of the selection method is to choose candidates whose Multiobjective Genetic mix will tend to lead to improved candidate solutions in the next generation. The common selection methods are random, Elitist [14], Roulette Wheel Tournament [15], etc. Multiobjective genetic operators provide mixing of chromosome portions from the parent or parents to form the offspring of the next generation. Examples of Multiobjective Genetic operators include crossover, mutation, inversion, etc [16]. The process repeats until either the number of iterations is exceeded or an acceptable solution is found. A generic view of a GA includes: a) Initialization of the initial population. b) Ranking is determined on the evaluation of the fitness functions on each chromosome. c) Mating rights are determined using application of the selection method on the population. d) Application of the multi-objective genetic operators on the chromosomes selected for mating. e) Return to Step #2. The following flowchart shows the main phases used to simulate the working of Multi-objective Genetic Algorithm [17]. 3. Applying Genetic Algorithm to FPGA Test Pattern Generation For applying Genetic Algorithm to FPGA circuit, it is first converted to CNF form. For a given gate, a CNF formula ᵠ is a set of clauses and is represented by conjunction of the CNF formulas of each gate. One of the most illustrious applications of SAT is FPGA routing [18], logic synthesis [19], Automatic Test Pattern Generation (ATPG) [20], testing include delay fault testing and redundancy identification and elimination, functional vector generation.to generate a test pattern for a single fault. a) First extract a formula that defines the set of test patterns that detect the fault. b) Then use a Boolean satisfiability algorithm to satisfy the formula. To extract a formula, a directed acyclic graph [21] is constructed as follows that represents the topological description of the circuit: a) The nodes of the graph are circuit inputs, outputs, gates, and fan-out points and the edges of the graph are circuit lines i.e. wires. b) The sources of the graph are circuit outputs c) And the sinks of the graph are the circuit inputs. Every edge has an associated variable. Fig shows an example circuit and its associated DAG [22]. Fig 2: Example Circuit [22] Fig 1: Flow Chart of Multi-objective genetic Algorithm
3 Chopra and Singh 3 Fig 3: Associated Directed Acyclic Graph (DAG) [22] 3.1 SAT Representation for FPGA Testing An individual is represented for a SAT instance with variables is a string of bits where each variable is associated to one bit [21]. The search space consist of the set S (i.e. all the possible strings n of bits) and an individual X& clearly corresponds to an assignment.x i represents the truth value of the ith atom and X [i α] represents an individual X& where the ith atom has been set to the value α0. Given an individual X& and a clause c, we use SAT(X, c) to denote the fact that the assignment associated X & satisfies the clause c. 3.2 Fitness Function Given a formula φ and an individual X, the fitness of X is defined to be the number of clauses which are not satisfied by X [119]. eval: S Ɲ eval (X) =card ({c sat(x,c)^ c ε φ}) Where card represents the cardinality of a set. This fitness function will be used in the selection process and stimulate an order on the population [124]. 3.3 Selection Process The selection process [23] takes as input a given population and extracts some individual s assignments according to that selection criterion. The selected individuals are the elected as parents for the evolution process and evolve by crossover operations. It is necessary to keep some diversity in the population to insure an efficient search so that if the selected parents are too close, some region of the search space will not be explored. This diversity of the selected population is achieved by introducing the notion of hamming distance between strings of bits. This distance gives the number of different bits between two strings and can be defined here as: ham: S*S Ɲ ham (X, Y) =card({ X i X X i Y i }) Therefore the function is defined as select: 2S * Ɲ* Ɲ 2S such that select (P, n, d) is the set of the n best X in P according to eval and such that X, Y select (P, n, d), ham(x, Y)>=d. typically, the first generation consists of randomly generated individuals. Next generation individuals are selected on the basis of their fitness values i.e. the more the individual is fit the more it has probability to come into the next generation. Then do crossover operation between the two individuals of the chosen ones. In this work two point and three point crossover is used to obtain the next 10 individuals. Fitness proportionate selection also known as roulette wheel selection is a genetic operator used in Genetic Algorithm for selecting potentially useful solutions for recombination. In this method fitness function assigns fitness to possible solutions or chromosomes which is used to associate a probability of selection with each individual chromosome. While candidate solution with a higher fitness will be less likely to be eliminated, there is still a chance that they may be. A
4 4 Chopra and Singh roulette wheel with each slice proportional in size to the fitness, [see table 1] shows the selection probability for 11 individuals, linear ranking and selective pressure of 2 together with the fitness value. Individual 1 is the fit individual and occupies the largest interval, whereas individual 10 as the second least fit individual has the smallest interval on the line. Individual 11, the least fit interval, has a fitness value of 0 and get no chance for reproduction. Table 1: Selection probability and fitness value For selecting the mating population the appropriate number of uniformly distributed random numbers (uniform distributed between 0.0 and 1.0) is independently generated [24]. Fig 4: Roulette wheel selection method [25] After selection the mating population consists of the individuals: 1, 2, and 3,5,6,9. The roulette-wheel selection algorithm provides a zero bias but does not guarantee minimum spread. 4. Conclusion This work has revealed that FPGA testing in the form of Boolean SAT, which is a NP complete problem can be proficiently solved using the Multi-Objective Genetic Algorithm by first converting it into the Boolean SAT and representing it as a constraint satisfaction problem. This simulation work reads the BLIF files representing the Xilinx MCNC Benchmark circuits as word file and dynamically circuit graphs are created for above used circuits. Then these graphs are used for FPGA circuit testing using GA procedure and results have shown that as the problem size are increased by increasing number of variables and clauses, the fault coverage ratio increases. Also as the numbers of generations are increased, fault coverage ratio and CPU time to test the circuit for each FPGA circuit has improved. 5. References 1) Görschwin Fey, Junhao Shi and Rolf Drechsler Efficiency of Multi-Valued Encoding in SAT-based ATPG 36th International Symposium on Multiple- Valued Logic Singapore. ISBN: , May 17-May ) Jie Qin A Brief Introduction to Application-Dependent FPGA Testing Dept. of Electrical and Computer Engineering 200 Broun Hall, Auburn University, AL ) Junhao Shi, Görschwin Fey,Rolf Drechsler, Andreas Glowatz, Jürgen Schlöffel and Friedrich Hapke Experimental Studies on SAT-Based Test Pattern Generation for Industrial Circuits 6th International Conference ASICON Vol.2, pp: ,24 Oct 2005.
5 Chopra and Singh 5 4) Paolo Prinetto, Maurizio Rebaudengo, and Matteo Soriza GATTO: A Genetic Algorithm for Automatic Test Pattern Generation for Large Synchronous Sequential Circuits IEEE Transactions on Computer Aided Design Of Integrated Circuits And Systems, Vol. 15, Issue No. 8, August, ) Gregor Papa, Tomasz Garbolino,Franc Novak and Andrzej H lawiczka Deterministic Test Pattern Generator Design With Genetic Algorithm Approach Journal of Electrical Engineering, Vol. 58, Issue No. 3, pp: , ) Jin-Kao Hao, Frédéric Lardeux and Frédéric Saubion A Hybrid Genetic Algorithm for the Satisfiability Problem LERIA, Bd Lavoisier, F Angers Cedex 01, ) Tracy Larrabee, member IEEE Test Pattern Generation using Boolean Satisfiability IEEE Transactions on Computer-Aided Design, Vol. 11, Issue No. 1, January ) J. Shi G. Fey R. Drechsler A. Glowatz F. Hapke J. Schlöffel PASSAT: Efficient SAT-based Test Pattern Generation for Industrial Circuits Proceedings of IEEE Computer Society Annual Symposium on VLSI, pp: , May ) V.D. Aggrawal Test Generation for MOS Circuits Using D-Algorithm 20th IEEE Conference on Design Automation pp: 64-70, June ) P. Goel, PODEM-X: An Automatic Test Generation System for VLSI Logic Structures 18th IEEE Conference on Design Automation pp: , 29 June ) Fujiwara, H. On the Acceleration of Test Generation Algorithms IEEE Transactions on Computers Vol. C -32, Issue No. 12, pp: , Dec ) Carlos A Coello. A Comparative survey of Evolutionary based Multiobjective Optimization International Journal of Knowledge and information Systems, Vol. 1, pp: , December ) Carlos M. Fonsecay, Peter J. Flemingz, An Overview of Evolutionary Algorithms in Multiobjective Optimization International Journal of Evolutionary Computation, Vol. 3, Issue No.1, pp 1-16,April 7, ) Kalyanmoy Deb, Associate Member IEEE, Amrit Pratap, Sameer Agarwal, and T. Meyarivan A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II IEEE Transaction on Evolutionary Computation, Vol.6, pp: , April ) Haiming Lu, Gary G.Yen Member IEEE Multiobjective Optimization Design using Genetic Algorithm Proceedings of the IEEE International Conference on Control Applications pp: ,september 5-7, ) Ujjwal Maulik, Sanghamitra Bandyopadhyay, Anirban Mukhopadhyay Multiobjective Genetic Algorithms for Clustering book published by Springer Heidelberg Dordrecht London New York, ISBN ,e-ISBN , ) Jin-Kao Hao, Frédéric Lardeux and Frédéric Saubion A Hybrid Genetic Algorithm for the Satisfiability Problem LERIA, Bd Lavoisier, F Angers Cedex 01, ) Gi-Joon Nam and Karem A. Sakallah. Detailed Routing of Complex FPGAs via Search-Based Boolean SAT Symposium on Field Programmable Gate Arrays, Monterey, CA, pp: , December ) L. Andrew C. Field Programmable Gate Array Logic Synthesis using Boolean Satisfiability, M. Tech. Thesis submitted to Graduate Department of Electrical and Computer Engineering Department, University of Toronto ) Fadi A. Aloul and Assim Sagahyroon SAT-Based Techniques in Test Vectors
6 6 Chopra and Singh Generation" International Journal of Advances in Information Technology, Vol. 1, Issue No. 4, November ) Fahiem Bacchus, Toby Walash (Eds.) Theory and Applications of Satisfiability Testing 8th Springer International Conference, SAT June 19-23, ) Y.L. Wu, S. Tsukiyama, and M. Marek-Sadowska, Graph Based Analysis of 2-D FPGA Routing IEEE Transactions on Computer-Aided Design, pp: 33-44, Jan ) Ujjwal Maulik, Sanghamitra Bandyopadhyay, Anirban Mukhopadhyay Multiobjective Genetic Algorithms for Clustering book published by Springer Heidelberg Dordrecht London New York, ISBN ,e-ISBN , ) Haiming Lu, Gary G.Yen Member IEEE Multiobjective Optimization Design using Genetic Algorithm Proceedings of the IEEE International Conference on Control Applications pp: ,september 5-7, ) Rolf Drechsler, Stephan Eggersglub, Gorschwin Fey and Daniel Tille SATbased Automatic Test Pattern Generation Dagstuhl Seminar Proceedings 08351, Evolutionary test Generation 2009.
ISSN: 2319-5967 ISO 9001:2008 Certified International Journal of Engineering Science and Innovative Technology (IJESIT) Volume 2, Issue 3, May 2013
Transistor Level Fault Finding in VLSI Circuits using Genetic Algorithm Lalit A. Patel, Sarman K. Hadia CSPIT, CHARUSAT, Changa., CSPIT, CHARUSAT, Changa Abstract This paper presents, genetic based algorithm
More informationEvolutionary SAT Solver (ESS)
Ninth LACCEI Latin American and Caribbean Conference (LACCEI 2011), Engineering for a Smart Planet, Innovation, Information Technology and Computational Tools for Sustainable Development, August 3-5, 2011,
More informationAlpha Cut based Novel Selection for Genetic Algorithm
Alpha Cut based Novel for Genetic Algorithm Rakesh Kumar Professor Girdhar Gopal Research Scholar Rajesh Kumar Assistant Professor ABSTRACT Genetic algorithm (GA) has several genetic operators that can
More informationElectric Distribution Network Multi objective Design Using Problem Specific Genetic Algorithm
Electric Distribution Network Multi objective Design Using Problem Specific Genetic Algorithm 1 Parita Vinodbhai Desai, 2 Jignesh Patel, 3 Sangeeta Jagdish Gurjar 1 Department of Electrical Engineering,
More informationA Robust Method for Solving Transcendental Equations
www.ijcsi.org 413 A Robust Method for Solving Transcendental Equations Md. Golam Moazzam, Amita Chakraborty and Md. Al-Amin Bhuiyan Department of Computer Science and Engineering, Jahangirnagar University,
More informationIntroduction To Genetic Algorithms
1 Introduction To Genetic Algorithms Dr. Rajib Kumar Bhattacharjya Department of Civil Engineering IIT Guwahati Email: rkbc@iitg.ernet.in References 2 D. E. Goldberg, Genetic Algorithm In Search, Optimization
More informationA Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II
182 IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, VOL. 6, NO. 2, APRIL 2002 A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II Kalyanmoy Deb, Associate Member, IEEE, Amrit Pratap, Sameer Agarwal,
More informationA Parallel Processor for Distributed Genetic Algorithm with Redundant Binary Number
A Parallel Processor for Distributed Genetic Algorithm with Redundant Binary Number 1 Tomohiro KAMIMURA, 2 Akinori KANASUGI 1 Department of Electronics, Tokyo Denki University, 07ee055@ms.dendai.ac.jp
More informationModel-based Parameter Optimization of an Engine Control Unit using Genetic Algorithms
Symposium on Automotive/Avionics Avionics Systems Engineering (SAASE) 2009, UC San Diego Model-based Parameter Optimization of an Engine Control Unit using Genetic Algorithms Dipl.-Inform. Malte Lochau
More informationA hybrid Approach of Genetic Algorithm and Particle Swarm Technique to Software Test Case Generation
A hybrid Approach of Genetic Algorithm and Particle Swarm Technique to Software Test Case Generation Abhishek Singh Department of Information Technology Amity School of Engineering and Technology Amity
More informationCollege of information technology Department of software
University of Babylon Undergraduate: third class College of information technology Department of software Subj.: Application of AI lecture notes/2011-2012 ***************************************************************************
More informationA Fast Computational Genetic Algorithm for Economic Load Dispatch
A Fast Computational Genetic Algorithm for Economic Load Dispatch M.Sailaja Kumari 1, M.Sydulu 2 Email: 1 Sailaja_matam@Yahoo.com 1, 2 Department of Electrical Engineering National Institute of Technology,
More informationNew Modifications of Selection Operator in Genetic Algorithms for the Traveling Salesman Problem
New Modifications of Selection Operator in Genetic Algorithms for the Traveling Salesman Problem Radovic, Marija; and Milutinovic, Veljko Abstract One of the algorithms used for solving Traveling Salesman
More informationGenetic Algorithms commonly used selection, replacement, and variation operators Fernando Lobo University of Algarve
Genetic Algorithms commonly used selection, replacement, and variation operators Fernando Lobo University of Algarve Outline Selection methods Replacement methods Variation operators Selection Methods
More informationNumerical Research on Distributed Genetic Algorithm with Redundant
Numerical Research on Distributed Genetic Algorithm with Redundant Binary Number 1 Sayori Seto, 2 Akinori Kanasugi 1,2 Graduate School of Engineering, Tokyo Denki University, Japan 10kme41@ms.dendai.ac.jp,
More informationManagement Science Letters
Management Science Letters 4 (2014) 905 912 Contents lists available at GrowingScience Management Science Letters homepage: www.growingscience.com/msl Measuring customer loyalty using an extended RFM and
More informationD A T A M I N I N G C L A S S I F I C A T I O N
D A T A M I N I N G C L A S S I F I C A T I O N FABRICIO VOZNIKA LEO NARDO VIA NA INTRODUCTION Nowadays there is huge amount of data being collected and stored in databases everywhere across the globe.
More informationON SUITABILITY OF FPGA BASED EVOLVABLE HARDWARE SYSTEMS TO INTEGRATE RECONFIGURABLE CIRCUITS WITH HOST PROCESSING UNIT
216 ON SUITABILITY OF FPGA BASED EVOLVABLE HARDWARE SYSTEMS TO INTEGRATE RECONFIGURABLE CIRCUITS WITH HOST PROCESSING UNIT *P.Nirmalkumar, **J.Raja Paul Perinbam, @S.Ravi and #B.Rajan *Research Scholar,
More informationGA as a Data Optimization Tool for Predictive Analytics
GA as a Data Optimization Tool for Predictive Analytics Chandra.J 1, Dr.Nachamai.M 2,Dr.Anitha.S.Pillai 3 1Assistant Professor, Department of computer Science, Christ University, Bangalore,India, chandra.j@christunivesity.in
More informationSOFTWARE TESTING STRATEGY APPROACH ON SOURCE CODE APPLYING CONDITIONAL COVERAGE METHOD
SOFTWARE TESTING STRATEGY APPROACH ON SOURCE CODE APPLYING CONDITIONAL COVERAGE METHOD Jaya Srivastaval 1 and Twinkle Dwivedi 2 1 Department of Computer Science & Engineering, Shri Ramswaroop Memorial
More informationCellular Automaton: The Roulette Wheel and the Landscape Effect
Cellular Automaton: The Roulette Wheel and the Landscape Effect Ioan Hălălae Faculty of Engineering, Eftimie Murgu University, Traian Vuia Square 1-4, 385 Reşiţa, Romania Phone: +40 255 210227, Fax: +40
More informationGenetic Algorithm. Based on Darwinian Paradigm. Intrinsically a robust search and optimization mechanism. Conceptual Algorithm
24 Genetic Algorithm Based on Darwinian Paradigm Reproduction Competition Survive Selection Intrinsically a robust search and optimization mechanism Slide -47 - Conceptual Algorithm Slide -48 - 25 Genetic
More informationGenetic Algorithm Performance with Different Selection Strategies in Solving TSP
Proceedings of the World Congress on Engineering Vol II WCE, July 6-8,, London, U.K. Genetic Algorithm Performance with Different Selection Strategies in Solving TSP Noraini Mohd Razali, John Geraghty
More informationA Method of Cloud Resource Load Balancing Scheduling Based on Improved Adaptive Genetic Algorithm
Journal of Information & Computational Science 9: 16 (2012) 4801 4809 Available at http://www.joics.com A Method of Cloud Resource Load Balancing Scheduling Based on Improved Adaptive Genetic Algorithm
More informationLab 4: 26 th March 2012. Exercise 1: Evolutionary algorithms
Lab 4: 26 th March 2012 Exercise 1: Evolutionary algorithms 1. Found a problem where EAs would certainly perform very poorly compared to alternative approaches. Explain why. Suppose that we want to find
More informationGenetic Algorithm Based Interconnection Network Topology Optimization Analysis
Genetic Algorithm Based Interconnection Network Topology Optimization Analysis 1 WANG Peng, 2 Wang XueFei, 3 Wu YaMing 1,3 College of Information Engineering, Suihua University, Suihua Heilongjiang, 152061
More informationAn Evolutionary Algorithm in Grid Scheduling by multiobjective Optimization using variants of NSGA
International Journal of Scientific and Research Publications, Volume 2, Issue 9, September 2012 1 An Evolutionary Algorithm in Grid Scheduling by multiobjective Optimization using variants of NSGA Shahista
More informationImplementation and Design of AES S-Box on FPGA
International Journal of Research in Engineering and Science (IJRES) ISSN (Online): 232-9364, ISSN (Print): 232-9356 Volume 3 Issue ǁ Jan. 25 ǁ PP.9-4 Implementation and Design of AES S-Box on FPGA Chandrasekhar
More informationPROCESS OF LOAD BALANCING IN CLOUD COMPUTING USING GENETIC ALGORITHM
PROCESS OF LOAD BALANCING IN CLOUD COMPUTING USING GENETIC ALGORITHM Md. Shahjahan Kabir 1, Kh. Mohaimenul Kabir 2 and Dr. Rabiul Islam 3 1 Dept. of CSE, Dhaka International University, Dhaka, Bangladesh
More informationInternational Journal of Software and Web Sciences (IJSWS) www.iasir.net
International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research) ISSN (Print): 2279-0063 ISSN (Online): 2279-0071 International
More informationPractical Applications of Evolutionary Computation to Financial Engineering
Hitoshi Iba and Claus C. Aranha Practical Applications of Evolutionary Computation to Financial Engineering Robust Techniques for Forecasting, Trading and Hedging 4Q Springer Contents 1 Introduction to
More informationAsexual Versus Sexual Reproduction in Genetic Algorithms 1
Asexual Versus Sexual Reproduction in Genetic Algorithms Wendy Ann Deslauriers (wendyd@alumni.princeton.edu) Institute of Cognitive Science,Room 22, Dunton Tower Carleton University, 25 Colonel By Drive
More informationVolume 3, Issue 2, February 2015 International Journal of Advance Research in Computer Science and Management Studies
Volume 3, Issue 2, February 2015 International Journal of Advance Research in Computer Science and Management Studies Research Article / Survey Paper / Case Study Available online at: www.ijarcsms.com
More informationKeywords: Information Retrieval, Vector Space Model, Database, Similarity Measure, Genetic Algorithm.
Volume 3, Issue 8, August 2013 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Effective Information
More informationAS part of the development process, software needs to
Dynamic White-Box Software Testing using a Recursive Hybrid Evolutionary Strategy/Genetic Algorithm Ashwin Panchapakesan, Graduate Student Member, Rami Abielmona, Senior Member, IEEE, and Emil Petriu,
More informationA Genetic Algorithm Processor Based on Redundant Binary Numbers (GAPBRBN)
ISSN: 2278 1323 All Rights Reserved 2014 IJARCET 3910 A Genetic Algorithm Processor Based on Redundant Binary Numbers (GAPBRBN) Miss: KIRTI JOSHI Abstract A Genetic Algorithm (GA) is an intelligent search
More informationArchitecture bits. (Chromosome) (Evolved chromosome) Downloading. Downloading PLD. GA operation Architecture bits
A Pattern Recognition System Using Evolvable Hardware Masaya Iwata 1 Isamu Kajitani 2 Hitoshi Yamada 2 Hitoshi Iba 1 Tetsuya Higuchi 1 1 1-1-4,Umezono,Tsukuba,Ibaraki,305,Japan Electrotechnical Laboratory
More informationGARDA: a Diagnostic ATPG for Large Synchronous Sequential Circuits
GARDA: a Diagnostic ATPG for Large Synchronous Sequential Circuits F. Corno, P. Prinetto, M. Rebaudengo, M. Sonza Reorda Politecnico di Torino Dipartimento di Automatica e Informatica Torino, Italy Abstract
More information14.10.2014. Overview. Swarms in nature. Fish, birds, ants, termites, Introduction to swarm intelligence principles Particle Swarm Optimization (PSO)
Overview Kyrre Glette kyrrehg@ifi INF3490 Swarm Intelligence Particle Swarm Optimization Introduction to swarm intelligence principles Particle Swarm Optimization (PSO) 3 Swarms in nature Fish, birds,
More informationGameTime: A Toolkit for Timing Analysis of Software
GameTime: A Toolkit for Timing Analysis of Software Sanjit A. Seshia and Jonathan Kotker EECS Department, UC Berkeley {sseshia,jamhoot}@eecs.berkeley.edu Abstract. Timing analysis is a key step in the
More informationInternational Journal of Computer & Organization Trends Volume21 Number1 June 2015 A Study on Load Balancing in Cloud Computing
A Study on Load Balancing in Cloud Computing * Parveen Kumar * Er.Mandeep Kaur Guru kashi University,Talwandi Sabo Guru kashi University,Talwandi Sabo Abstract: Load Balancing is a computer networking
More informationA Service Revenue-oriented Task Scheduling Model of Cloud Computing
Journal of Information & Computational Science 10:10 (2013) 3153 3161 July 1, 2013 Available at http://www.joics.com A Service Revenue-oriented Task Scheduling Model of Cloud Computing Jianguang Deng a,b,,
More informationHPGAST: High Performance GA-based Sequential circuits Test generation on Beowulf PC-Cluster
HPGAST: High Performance GA-based Sequential circuits Test generation on Beowulf PC-Cluster Tepakorn Siriwan Pradondet Nilagupta Department of Computer Engineering, Kasetsart University 50 Pahonyothin
More informationA SURVEY ON GENETIC ALGORITHM FOR INTRUSION DETECTION SYSTEM
A SURVEY ON GENETIC ALGORITHM FOR INTRUSION DETECTION SYSTEM MS. DIMPI K PATEL Department of Computer Science and Engineering, Hasmukh Goswami college of Engineering, Ahmedabad, Gujarat ABSTRACT The Internet
More informationA New Multi-objective Evolutionary Optimisation Algorithm: The Two-Archive Algorithm
A New Multi-objective Evolutionary Optimisation Algorithm: The Two-Archive Algorithm Kata Praditwong 1 and Xin Yao 2 The Centre of Excellence for Research in Computational Intelligence and Applications(CERCIA),
More informationEffective Estimation Software cost using Test Generations
Asia-pacific Journal of Multimedia Services Convergence with Art, Humanities and Sociology Vol.1, No.1 (2011), pp. 1-10 http://dx.doi.org/10.14257/ajmscahs.2011.06.01 Effective Estimation Software cost
More informationA STUDY OF TASK SCHEDULING IN MULTIPROCESSOR ENVIROMENT Ranjit Rajak 1, C.P.Katti 2, Nidhi Rajak 3
A STUDY OF TASK SCHEDULING IN MULTIPROCESSOR ENVIROMENT Ranjit Rajak 1, C.P.Katti, Nidhi Rajak 1 Department of Computer Science & Applications, Dr.H.S.Gour Central University, Sagar, India, ranjit.jnu@gmail.com
More informationTowards Heuristic Web Services Composition Using Immune Algorithm
Towards Heuristic Web Services Composition Using Immune Algorithm Jiuyun Xu School of Computer & Communication Engineering China University of Petroleum xujiuyun@ieee.org Stephan Reiff-Marganiec Department
More informationOptimised Realistic Test Input Generation
Optimised Realistic Test Input Generation Mustafa Bozkurt and Mark Harman {m.bozkurt,m.harman}@cs.ucl.ac.uk CREST Centre, Department of Computer Science, University College London. Malet Place, London
More informationHow To Fix A 3 Bit Error In Data From A Data Point To A Bit Code (Data Point) With A Power Source (Data Source) And A Power Cell (Power Source)
FPGA IMPLEMENTATION OF 4D-PARITY BASED DATA CODING TECHNIQUE Vijay Tawar 1, Rajani Gupta 2 1 Student, KNPCST, Hoshangabad Road, Misrod, Bhopal, Pin no.462047 2 Head of Department (EC), KNPCST, Hoshangabad
More informationPackage NHEMOtree. February 19, 2015
Type Package Package NHEMOtree February 19, 2015 Title Non-hierarchical evolutionary multi-objective tree learner to perform cost-sensitive classification Depends partykit, emoa, sets, rpart Version 1.0
More informationFault Modeling. Why model faults? Some real defects in VLSI and PCB Common fault models Stuck-at faults. Transistor faults Summary
Fault Modeling Why model faults? Some real defects in VLSI and PCB Common fault models Stuck-at faults Single stuck-at faults Fault equivalence Fault dominance and checkpoint theorem Classes of stuck-at
More informationprocessed parallely over the cluster nodes. Mapreduce thus provides a distributed approach to solve complex and lengthy problems
Big Data Clustering Using Genetic Algorithm On Hadoop Mapreduce Nivranshu Hans, Sana Mahajan, SN Omkar Abstract: Cluster analysis is used to classify similar objects under same group. It is one of the
More informationInternational Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) www.iasir.net
International Association of Scientific Innovation and Research (IASIR) (An Association Unifying the Sciences, Engineering, and Applied Research) International Journal of Emerging Technologies in Computational
More informationEvaluation of Different Task Scheduling Policies in Multi-Core Systems with Reconfigurable Hardware
Evaluation of Different Task Scheduling Policies in Multi-Core Systems with Reconfigurable Hardware Mahyar Shahsavari, Zaid Al-Ars, Koen Bertels,1, Computer Engineering Group, Software & Computer Technology
More informationGenetic Algorithms for Bridge Maintenance Scheduling. Master Thesis
Genetic Algorithms for Bridge Maintenance Scheduling Yan ZHANG Master Thesis 1st Examiner: Prof. Dr. Hans-Joachim Bungartz 2nd Examiner: Prof. Dr. rer.nat. Ernst Rank Assistant Advisor: DIPL.-ING. Katharina
More informationModified Version of Roulette Selection for Evolution Algorithms - the Fan Selection
Modified Version of Roulette Selection for Evolution Algorithms - the Fan Selection Adam S lowik, Micha l Bia lko Department of Electronic, Technical University of Koszalin, ul. Śniadeckich 2, 75-453 Koszalin,
More informationMulti-Objective Optimization using Evolutionary Algorithms
Multi-Objective Optimization using Evolutionary Algorithms Kalyanmoy Deb Department of Mechanical Engineering, Indian Institute of Technology, Kanpur, India JOHN WILEY & SONS, LTD Chichester New York Weinheim
More informationMulti-Objective Optimization to Workflow Grid Scheduling using Reference Point based Evolutionary Algorithm
Multi-Objective Optimization to Workflow Grid Scheduling using Reference Point based Evolutionary Algorithm Ritu Garg Assistant Professor Computer Engineering Department National Institute of Technology,
More informationHybrid Evolution of Heterogeneous Neural Networks
Hybrid Evolution of Heterogeneous Neural Networks 01001110 01100101 01110101 01110010 01101111 01101110 01101111 01110110 01100001 00100000 01110011 01101011 01110101 01110000 01101001 01101110 01100001
More informationCLOUD DATABASE ROUTE SCHEDULING USING COMBANATION OF PARTICLE SWARM OPTIMIZATION AND GENETIC ALGORITHM
CLOUD DATABASE ROUTE SCHEDULING USING COMBANATION OF PARTICLE SWARM OPTIMIZATION AND GENETIC ALGORITHM *Shabnam Ghasemi 1 and Mohammad Kalantari 2 1 Deparment of Computer Engineering, Islamic Azad University,
More informationA Novel Binary Particle Swarm Optimization
Proceedings of the 5th Mediterranean Conference on T33- A Novel Binary Particle Swarm Optimization Motaba Ahmadieh Khanesar, Member, IEEE, Mohammad Teshnehlab and Mahdi Aliyari Shoorehdeli K. N. Toosi
More informationA Multi-Objective Performance Evaluation in Grid Task Scheduling using Evolutionary Algorithms
A Multi-Objective Performance Evaluation in Grid Task Scheduling using Evolutionary Algorithms MIGUEL CAMELO, YEZID DONOSO, HAROLD CASTRO Systems and Computer Engineering Department Universidad de los
More informationMemory Allocation Technique for Segregated Free List Based on Genetic Algorithm
Journal of Al-Nahrain University Vol.15 (2), June, 2012, pp.161-168 Science Memory Allocation Technique for Segregated Free List Based on Genetic Algorithm Manal F. Younis Computer Department, College
More informationAn Alternative Archiving Technique for Evolutionary Polygonal Approximation
An Alternative Archiving Technique for Evolutionary Polygonal Approximation José Luis Guerrero, Antonio Berlanga and José Manuel Molina Computer Science Department, Group of Applied Artificial Intelligence
More informationOn heijunka design of assembly load balancing problem: Genetic algorithm & ameliorative procedure-combined approach
International Journal of Intelligent Information Systems 2015; 4(2-1): 49-58 Published online February 10, 2015 (http://www.sciencepublishinggroup.com/j/ijiis) doi: 10.11648/j.ijiis.s.2015040201.17 ISSN:
More informationMultiobjective Multicast Routing Algorithm
Multiobjective Multicast Routing Algorithm Jorge Crichigno, Benjamín Barán P. O. Box 9 - National University of Asunción Asunción Paraguay. Tel/Fax: (+9-) 89 {jcrichigno, bbaran}@cnc.una.py http://www.una.py
More informationFeature Selection using Integer and Binary coded Genetic Algorithm to improve the performance of SVM Classifier
Feature Selection using Integer and Binary coded Genetic Algorithm to improve the performance of SVM Classifier D.Nithya a, *, V.Suganya b,1, R.Saranya Irudaya Mary c,1 Abstract - This paper presents,
More informationFault Analysis in Software with the Data Interaction of Classes
, pp.189-196 http://dx.doi.org/10.14257/ijsia.2015.9.9.17 Fault Analysis in Software with the Data Interaction of Classes Yan Xiaobo 1 and Wang Yichen 2 1 Science & Technology on Reliability & Environmental
More informationMaster's projects at ITMO University. Daniil Chivilikhin PhD Student @ ITMO University
Master's projects at ITMO University Daniil Chivilikhin PhD Student @ ITMO University General information Guidance from our lab's researchers Publishable results 2 Research areas Research at ITMO Evolutionary
More informationAn Efficient Approach for Task Scheduling Based on Multi-Objective Genetic Algorithm in Cloud Computing Environment
IJCSC VOLUME 5 NUMBER 2 JULY-SEPT 2014 PP. 110-115 ISSN-0973-7391 An Efficient Approach for Task Scheduling Based on Multi-Objective Genetic Algorithm in Cloud Computing Environment 1 Sourabh Budhiraja,
More informationInternational Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering
DOI: 10.15662/ijareeie.2014.0307061 Economic Dispatch of Power System Optimization with Power Generation Schedule Using Evolutionary Technique Girish Kumar 1, Rameshwar singh 2 PG Student [Control system],
More informationEstimation of the COCOMO Model Parameters Using Genetic Algorithms for NASA Software Projects
Journal of Computer Science 2 (2): 118-123, 2006 ISSN 1549-3636 2006 Science Publications Estimation of the COCOMO Model Parameters Using Genetic Algorithms for NASA Software Projects Alaa F. Sheta Computers
More informationA Genetic Algorithm Approach for Solving a Flexible Job Shop Scheduling Problem
A Genetic Algorithm Approach for Solving a Flexible Job Shop Scheduling Problem Sayedmohammadreza Vaghefinezhad 1, Kuan Yew Wong 2 1 Department of Manufacturing & Industrial Engineering, Faculty of Mechanical
More informationSimple Population Replacement Strategies for a Steady-State Multi-Objective Evolutionary Algorithm
Simple Population Replacement Strategies for a Steady-State Multi-Objective Evolutionary Christine L. Mumford School of Computer Science, Cardiff University PO Box 916, Cardiff CF24 3XF, United Kingdom
More informationTRUE SINGLE PHASE CLOCKING BASED FLIP-FLOP DESIGN
TRUE SINGLE PHASE CLOCKING BASED FLIP-FLOP DESIGN USING DIFFERENT FOUNDRIES Priyanka Sharma 1 and Rajesh Mehra 2 1 ME student, Department of E.C.E, NITTTR, Chandigarh, India 2 Associate Professor, Department
More informationWeb Cluster Dynamic Load Balancing- GA Approach
Web Cluster Dynamic Load Balancing- GA Approach Chin Wen Cheong FOSEE, MultiMedia University 7545 Buit Beruang Malacca, Malaysia wcchin@mmu.edu.my Amy Lim Hui Lan Faculty of Information Technology MultiMedia
More informationA Novel Constraint Handling Strategy for Expensive Optimization Problems
th World Congress on Structural and Multidisciplinary Optimization 7 th - 2 th, June 25, Sydney Australia A Novel Constraint Handling Strategy for Expensive Optimization Problems Kalyan Shankar Bhattacharjee,
More informationEmpirically Identifying the Best Genetic Algorithm for Covering Array Generation
Empirically Identifying the Best Genetic Algorithm for Covering Array Generation Liang Yalan 1, Changhai Nie 1, Jonathan M. Kauffman 2, Gregory M. Kapfhammer 2, Hareton Leung 3 1 Department of Computer
More informationA Non-Linear Schema Theorem for Genetic Algorithms
A Non-Linear Schema Theorem for Genetic Algorithms William A Greene Computer Science Department University of New Orleans New Orleans, LA 70148 bill@csunoedu 504-280-6755 Abstract We generalize Holland
More informationStatic-Noise-Margin Analysis of Conventional 6T SRAM Cell at 45nm Technology
Static-Noise-Margin Analysis of Conventional 6T SRAM Cell at 45nm Technology Nahid Rahman Department of electronics and communication FET-MITS (Deemed university), Lakshmangarh, India B. P. Singh Department
More informationNew binary representation in Genetic Algorithms for solving TSP by mapping permutations to a list of ordered numbers
Proceedings of the 5th WSEAS Int Conf on COMPUTATIONAL INTELLIGENCE, MAN-MACHINE SYSTEMS AND CYBERNETICS, Venice, Italy, November 0-, 006 363 New binary representation in Genetic Algorithms for solving
More informationA Brief Study of the Nurse Scheduling Problem (NSP)
A Brief Study of the Nurse Scheduling Problem (NSP) Lizzy Augustine, Morgan Faer, Andreas Kavountzis, Reema Patel Submitted Tuesday December 15, 2009 0. Introduction and Background Our interest in the
More informationCombinational Controllability Controllability Formulas (Cont.)
Outline Digital Testing: Testability Measures The case for DFT Testability Measures Controllability and observability SCOA measures Combinational circuits Sequential circuits Adhoc techniques Easily testable
More informationHelical Antenna Optimization Using Genetic Algorithms
Helical Antenna Optimization Using Genetic Algorithms by Raymond L. Lovestead Thesis submitted to the Faculty of the Virginia Polytechnic Institute and State University in partial fulfillment of the requirements
More informationAN ADAPTIVE DISTRIBUTED LOAD BALANCING TECHNIQUE FOR CLOUD COMPUTING
AN ADAPTIVE DISTRIBUTED LOAD BALANCING TECHNIQUE FOR CLOUD COMPUTING Gurpreet Singh M.Phil Research Scholar, Computer Science Dept. Punjabi University, Patiala gurpreet.msa@gmail.com Abstract: Cloud Computing
More informationA Review And Evaluations Of Shortest Path Algorithms
A Review And Evaluations Of Shortest Path Algorithms Kairanbay Magzhan, Hajar Mat Jani Abstract: Nowadays, in computer networks, the routing is based on the shortest path problem. This will help in minimizing
More informationGenetic Algorithm Evolution of Cellular Automata Rules for Complex Binary Sequence Prediction
Brill Academic Publishers P.O. Box 9000, 2300 PA Leiden, The Netherlands Lecture Series on Computer and Computational Sciences Volume 1, 2005, pp. 1-6 Genetic Algorithm Evolution of Cellular Automata Rules
More informationPredictive Analytics using Genetic Algorithm for Efficient Supply Chain Inventory Optimization
182 IJCSNS International Journal of Computer Science and Network Security, VOL.10 No.3, March 2010 Predictive Analytics using Genetic Algorithm for Efficient Supply Chain Inventory Optimization P.Radhakrishnan
More informationProposal and Analysis of Stock Trading System Using Genetic Algorithm and Stock Back Test System
Proposal and Analysis of Stock Trading System Using Genetic Algorithm and Stock Back Test System Abstract: In recent years, many brokerage firms and hedge funds use a trading system based on financial
More informationCHAPTER 6 GENETIC ALGORITHM OPTIMIZED FUZZY CONTROLLED MOBILE ROBOT
77 CHAPTER 6 GENETIC ALGORITHM OPTIMIZED FUZZY CONTROLLED MOBILE ROBOT 6.1 INTRODUCTION The idea of evolutionary computing was introduced by (Ingo Rechenberg 1971) in his work Evolutionary strategies.
More informationNonlinear Model Predictive Control of Hammerstein and Wiener Models Using Genetic Algorithms
Nonlinear Model Predictive Control of Hammerstein and Wiener Models Using Genetic Algorithms Al-Duwaish H. and Naeem, Wasif Electrical Engineering Department/King Fahd University of Petroleum and Minerals
More informationKeywords revenue management, yield management, genetic algorithm, airline reservation
Volume 4, Issue 1, January 2014 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com A Revenue Management
More informationSimulating the Multiple Time-Period Arrival in Yield Management
Simulating the Multiple Time-Period Arrival in Yield Management P.K.Suri #1, Rakesh Kumar #2, Pardeep Kumar Mittal #3 #1 Dean(R&D), Chairman & Professor(CSE/IT/MCA), H.C.T.M., Kaithal(Haryana), India #2
More informationNature of Real-World Multi-objective Vehicle Routing with Evolutionary Algorithms
Nature of Real-World Multi-objective Vehicle Routing with Evolutionary Algorithms Juan Castro-Gutierrez, Dario Landa-Silva ASAP Research Group, School of Computer Science University of Nottingham, UK jpc@cs.nott.ac.uk,
More informationVol. 35, No. 3, Sept 30,2000 ملخص تعتبر الخوارزمات الجينية واحدة من أفضل طرق البحث من ناحية األداء. فبالرغم من أن استخدام هذه الطريقة ال يعطي الحل
AIN SHAMS UNIVERSITY FACULTY OF ENGINEERING Vol. 35, No. 3, Sept 30,2000 SCIENTIFIC BULLETIN Received on : 3/9/2000 Accepted on: 28/9/2000 pp : 337-348 GENETIC ALGORITHMS AND ITS USE WITH BACK- PROPAGATION
More informationAN APPROACH FOR SOFTWARE TEST CASE SELECTION USING HYBRID PSO
INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS ISSN 2320-7345 AN APPROACH FOR SOFTWARE TEST CASE SELECTION USING HYBRID PSO 1 Preeti Bala Thakur, 2 Prof. Toran Verma 1 Dept. of
More informationHYBRID GENETIC ALGORITHM PARAMETER EFFECTS FOR OPTIMIZATION OF CONSTRUCTION RESOURCE ALLOCATION PROBLEM. Jin-Lee KIM 1, M. ASCE
1560 HYBRID GENETIC ALGORITHM PARAMETER EFFECTS FOR OPTIMIZATION OF CONSTRUCTION RESOURCE ALLOCATION PROBLEM Jin-Lee KIM 1, M. ASCE 1 Assistant Professor, Department of Civil Engineering and Construction
More informationEvolutionary Prefetching and Caching in an Independent Storage Units Model
Evolutionary Prefetching and Caching in an Independent Units Model Athena Vakali Department of Informatics Aristotle University of Thessaloniki, Greece E-mail: avakali@csdauthgr Abstract Modern applications
More informationOptimising the resource utilisation in high-speed network intrusion detection systems.
Optimising the resource utilisation in high-speed network intrusion detection systems. Gerald Tripp www.kent.ac.uk Network intrusion detection Network intrusion detection systems are provided to detect
More information