This document discusses using genetic algorithms and particle swarm optimization to optimize software testing by finding the most error-prone paths in a program. It provides an overview of genetic algorithms and particle swarm optimization, describing how they can be applied to generate test cases to discover faults. The paper implements both genetic algorithms and particle swarm optimization on sample problems to find optimal solutions and compares the two approaches. It finds that while genetic algorithms can get trapped in local optima, particle swarm optimization tracks personal and global best positions to move toward global optima without getting stuck.
This paper presents a set of methods that uses a genetic algorithm for automatic test-data generation in
software testing. For several years researchers have proposed several methods for generating test data
which had different drawbacks. In this paper, we have presented various Genetic Algorithm (GA) based test
methods which will be having different parameters to automate the structural-oriented test data generation
on the basis of internal program structure. The factors discovered are used in evaluating the fitness
function of Genetic algorithm for selecting the best possible Test method. These methods take the test
populations as an input and then evaluate the test cases for that program. This integration will help in
improving the overall performance of genetic algorithm in search space exploration and exploitation fields
with better convergence rate.
Dynamic Radius Species Conserving Genetic Algorithm for Test Generation for S...ijseajournal
This paper critically examined nine different software models for modeling / developing multi-agent based
systems. The study revealed that the different models examined have their various advantages /
disadvantages and uniqueness in terms of practical development/deployment of the multi-agent based
information system in question. The agentology model is one methodology that can be easily adopted or
adapted for the development of any multi-agent based software prototype because it was inspired by the
best practices and good ideas contained in other agent oriented methodologies like CoMoMas, MASE,
GAIA, Prometheus, HIM, MASim and Tropos.
Artificial Intelligence in Robot Path Planningiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Software test-case generation is the process of identifying a set of test cases. It is necessary to generate the test sequence that satisfies the testing criteria. For solving this kind of difficult problem there were a lot of research works, which have been done in the past. The length of the test sequence plays an important role in software testing. The length of test sequence decides whether the sufficient testing is carried or not. Many existing test sequence generation techniques uses genetic algorithm for test-case generation in software testing. The Genetic Algorithm (GA) is an optimization heuristic technique that is implemented through evolution and fitness function. It generates new test cases from the existing test sequence. Further to improve the existing techniques, a new technique is proposed in this paper which combines the tabu search algorithm and the genetic algorithm. The hybrid technique combines the strength of the two meta-heuristic methods and produces efficient test- case sequence.
Introduction to Optimization with Genetic Algorithm (GA)Ahmed Gad
Selection of the optimal parameters for machine learning tasks is challenging. Some results may be bad not because the data is noisy or the used learning algorithm is weak, but due to the bad selection of the parameters values. This article gives a brief introduction about evolutionary algorithms (EAs) and describes genetic algorithm (GA) which is one of the simplest random-based EAs.
References:
Eiben, Agoston E., and James E. Smith. Introduction to evolutionary computing. Vol. 53. Heidelberg: springer, 2003.
https://www.linkedin.com/pulse/introduction-optimization-genetic-algorithm-ahmed-gad
https://www.kdnuggets.com/2018/03/introduction-optimization-with-genetic-algorithm.html
A Genetic Algorithm on Optimization Test FunctionsIJMERJOURNAL
ABSTRACT: Genetic Algorithms (GAs) have become increasingly useful over the years for solving combinatorial problems. Though they are generally accepted to be good performers among metaheuristic algorithms, most works have concentrated on the application of the GAs rather than the theoretical justifications. In this paper, we examine and justify the suitability of Genetic Algorithms in solving complex, multi-variable and multi-modal optimization problems. To achieve this, a simple Genetic Algorithm was used to solve four standard complicated optimization test functions, namely Rosenbrock, Schwefel, Rastrigin and Shubert functions. These functions are benchmarks to test the quality of an optimization procedure towards a global optimum. We show that the method has a quicker convergence to the global optima and that the optimal values for the Rosenbrock, Rastrigin, Schwefel and Shubert functions are zero (0), zero (0), -418.9829 and -14.5080 respectively
This paper presents a set of methods that uses a genetic algorithm for automatic test-data generation in
software testing. For several years researchers have proposed several methods for generating test data
which had different drawbacks. In this paper, we have presented various Genetic Algorithm (GA) based test
methods which will be having different parameters to automate the structural-oriented test data generation
on the basis of internal program structure. The factors discovered are used in evaluating the fitness
function of Genetic algorithm for selecting the best possible Test method. These methods take the test
populations as an input and then evaluate the test cases for that program. This integration will help in
improving the overall performance of genetic algorithm in search space exploration and exploitation fields
with better convergence rate.
Dynamic Radius Species Conserving Genetic Algorithm for Test Generation for S...ijseajournal
This paper critically examined nine different software models for modeling / developing multi-agent based
systems. The study revealed that the different models examined have their various advantages /
disadvantages and uniqueness in terms of practical development/deployment of the multi-agent based
information system in question. The agentology model is one methodology that can be easily adopted or
adapted for the development of any multi-agent based software prototype because it was inspired by the
best practices and good ideas contained in other agent oriented methodologies like CoMoMas, MASE,
GAIA, Prometheus, HIM, MASim and Tropos.
Artificial Intelligence in Robot Path Planningiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Software test-case generation is the process of identifying a set of test cases. It is necessary to generate the test sequence that satisfies the testing criteria. For solving this kind of difficult problem there were a lot of research works, which have been done in the past. The length of the test sequence plays an important role in software testing. The length of test sequence decides whether the sufficient testing is carried or not. Many existing test sequence generation techniques uses genetic algorithm for test-case generation in software testing. The Genetic Algorithm (GA) is an optimization heuristic technique that is implemented through evolution and fitness function. It generates new test cases from the existing test sequence. Further to improve the existing techniques, a new technique is proposed in this paper which combines the tabu search algorithm and the genetic algorithm. The hybrid technique combines the strength of the two meta-heuristic methods and produces efficient test- case sequence.
Introduction to Optimization with Genetic Algorithm (GA)Ahmed Gad
Selection of the optimal parameters for machine learning tasks is challenging. Some results may be bad not because the data is noisy or the used learning algorithm is weak, but due to the bad selection of the parameters values. This article gives a brief introduction about evolutionary algorithms (EAs) and describes genetic algorithm (GA) which is one of the simplest random-based EAs.
References:
Eiben, Agoston E., and James E. Smith. Introduction to evolutionary computing. Vol. 53. Heidelberg: springer, 2003.
https://www.linkedin.com/pulse/introduction-optimization-genetic-algorithm-ahmed-gad
https://www.kdnuggets.com/2018/03/introduction-optimization-with-genetic-algorithm.html
A Genetic Algorithm on Optimization Test FunctionsIJMERJOURNAL
ABSTRACT: Genetic Algorithms (GAs) have become increasingly useful over the years for solving combinatorial problems. Though they are generally accepted to be good performers among metaheuristic algorithms, most works have concentrated on the application of the GAs rather than the theoretical justifications. In this paper, we examine and justify the suitability of Genetic Algorithms in solving complex, multi-variable and multi-modal optimization problems. To achieve this, a simple Genetic Algorithm was used to solve four standard complicated optimization test functions, namely Rosenbrock, Schwefel, Rastrigin and Shubert functions. These functions are benchmarks to test the quality of an optimization procedure towards a global optimum. We show that the method has a quicker convergence to the global optima and that the optimal values for the Rosenbrock, Rastrigin, Schwefel and Shubert functions are zero (0), zero (0), -418.9829 and -14.5080 respectively
One of the obstacles that hinder the usage of mutation testing is its impracticality, two main contributors of this are a large number of mutants and a large number of test cases involves in the process. Researcher usually tries to address this problem by optimizing the mutants and the test case separately. In this research, we try to tackle both of optimizing mutant and optimizing test-case simultaneousl y using a coevolution optimization method. The coevolution optimization method is chosen for the mutation testing problem because the method works by optimizing multiple collections (population) of a solution. This research found that coevolution is better suited for multiproblem optimization than other single population methods (i.e. Genetic Algorithm), we also propose new indicator to determine the optimal coevolution cycle. The experiment is done to the artificial case, laboratory, and also a real case.
How to optimize with the help of the Particle Swarm Optimization Technique and xlOptimizer ? This brief tutorial will enable you to solve any optimization problem with the application of Particle Swarm Optimization Method. After a brief introduction about the method the tutorial will show you the steps that you will need to follow for application of PSO in optimization even if you do not know any programming.(Some basic knowledge of MS Excel 2010 and later is required).
EVOLUTIONARY COMPUTING TECHNIQUES FOR SOFTWARE EFFORT ESTIMATIONijcsit
Reliable and accurate estimation of software has always been a matter of concern for industry and academia. Numerous estimation models have been proposed by researchers, but no model is suitable for all types of datasets and environments. Since the motive of estimation model is to minimize the gap between actual and estimated effort, the effort estimation process can be viewed as an optimization problem to tune
the parameters. In this paper, evolutionary computing techniques, including, Bee colony optimization, Particle swarm optimization and Ant colony optimization have been employed to tune the parameters of COCOMO Model. The performance of these techniques has been analysed by established performance measure. The results obtained have been validated by using data of Interactive voice response (IVR)
projects. Evolutionary techniques have been found to be more accurate than existing estimation models.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Data Science - Part XIV - Genetic AlgorithmsDerek Kane
This lecture provides an overview on biological evolution and genetic algorithms in a machine learning context. We will start off by going through a broad overview of the biological evolutionary process and then explore how genetic algorithms can be developed that mimic these processes. We will dive into the types of problems that can be solved with genetic algorithms and then we will conclude with a series of practical examples in R which highlights the techniques: The Knapsack Problem, Feature Selection and OLS regression, and constrained optimizations.
A Non-Revisiting Genetic Algorithm for Optimizing Numeric Multi-Dimensional F...ijcsa
Genetic Algorithm (GA) is a robust and popular stochastic optimization algorithm for large and complex search spaces. The major shortcomings of Genetic Algorithms are premature convergence and revisits to individual solutions in the search space. In other words, Genetic algorithm is a revisiting algorithm that escorts to duplicate function evaluations which is a clear wastage of time and computational resources. In this paper, a non-revisiting genetic algorithm with adaptive mutation is proposed for the domain of MultiDimensional numeric function optimization. In this algorithm whenever a revisit occurs, the underlined search point is replaced with a mutated version of the best/random (chosen probabilistically) individual from the GA population. Furthermore, the recommended approach is not using any extra memory resources to avoid revisits. To analyze the influence of the method, the proposed non-revisiting algorithm is evaluated using nine benchmarks functions with two and four dimensions. The performance of the proposed genetic algorithm is superior as contrasted to simple genetic algorithm as confirmed by the experimental results.
Presented at Artificial Intelligence and Machine Learning for Advanced Drug Discovery & Development 2019 on 28th May 2019 by Dr Ed Griffen of MedChemica Ltd
For three decades, many mathematical programming methods have been developed to solve optimization problems. However, until now, there has not been a single totally efficient and robust method to coverall optimization problems that arise in the different engineering fields.Most engineering application design problems involve the choice of design variable values that better describe the behaviour of a system.At the same time, those results should cover the requirements and specifications imposed by the norms for that system. This last condition leads to predicting what the entrance parameter values should be whose design results comply with the norms and also present good performance, which describes the inverse problem.Generally, in design problems the variables are discreet from the mathematical point of view. However, most mathematical optimization applications are focused and developed for continuous variables. Presently, there are many research articles about optimization methods; the typical ones are based on calculus,numerical methods, and random methods.
The calculus-based methods have been intensely studied and are subdivided in two main classes: 1) the direct search methods find a local maximum moving a function over the relative local gradient directions and 2) the indirect methods usually find the local ends solving a set of non-linear equations, resultant of equating the gradient from the object function to zero, i.e., by means of multidimensional generalization of the notion of the function’s extreme points from elementary calculus given smooth function without restrictions to find a possible maximum which is to be restricted to those points whose slope is zero in all directions. The real world has many discontinuities and noisy spaces, which is why it is not surprising that the methods depending upon the restrictive requirements of continuity and existence of a derivative, are unsuitable for all, but a very limited problem domain. A number of schemes have been applied in many forms and sizes. The idea is quite direct inside a finite search space or a discrete infinite search space, where the algorithms can locate the object function values in each space point one at a time. The simplicity of this kind of algorithm is very attractive when the numbers of possibilities are very small. Nevertheless, these outlines are often inefficient, since they do not complete the requirements of robustness in big or highly-dimensional spaces, making it quite a hard task to find the optimal values. Given the shortcomings of the calculus-based techniques and the numerical ones the random methods have increased their popularity.
AUTOMATIC GENERATION AND OPTIMIZATION OF TEST DATA USING HARMONY SEARCH ALGOR...csandit
Software testing is the primary phase, which is performed during software development and it is
carried by a sequence of instructions of test inputs followed by expected output. The Harmony
Search (HS) algorithm is based on the improvisation process of music. In comparison to other
algorithms, the HSA has gain popularity and superiority in the field of evolutionary
computation. When musicians compose the harmony through different possible combinations of
the music, at that time the pitches are stored in the harmony memory and the optimization can
be done by adjusting the input pitches and generate the perfect harmony. The test case
generation process is used to identify test cases with resources and also identifies critical
domain requirements. In this paper, the role of Harmony search meta-heuristic search
technique is analyzed in generating random test data and optimized those test data. Test data
are generated and optimized by applying in a case study i.e. a withdrawal task in Bank ATM
through Harmony search. It is observed that this algorithm generates suitable test cases as well
as test data and gives brief details about the Harmony search method. It is used for test data
generation and optimization
The potential role of ai in the minimisation and mitigation of project delayPieter Rautenbach
Artificial intelligence (AI) can have wide reaching application within the construction
industry, however, the actual application of this set of technologies is currently under exploited. This
paper considers the role that the application of AI can take in optimising the efficiencies of project
execution and how this can potentially reduce project duration and minimise and mitigate delay on
projects.
Application of Genetic Algorithm in Software Engineering: A ReviewIRJESJOURNAL
Abstract. The software engineering is comparatively new and regularly changing field. The big challenge of meeting strict project schedules with high quality software requires that the field of software engineering be automated to large extent and human resource intervention be minimized to optimum level. To achieve this goal the researcher have explored the potential of machine learning approaches as they are adaptable, have learning ability. In this paper, we take a look at how genetic algorithm (GA) can be used to build tool for software development and maintenance tasks.
Comparative Study on Self Confidence among University Level Football, Kho-Kho...IOSR Journals
Abstract: Self-confidence is the belief that one can successfully perform or desired behavior. It is an important
factor to achieve our ultimate goal. Football, Kho-Kho and Kabaddi are very famous game in India involving
various physical fitness components like speed, co-ordination, power, strength, agility etc. The Researcher was
very keen to find out the self-confidence level of North Bengal University Football, Kho-Kho and Kabaddi
players before attending the inter university tournament. The purpose of this study was to compare the selfconfidence
level between Football, Kho-Kho and Kabaddi players. Eighteen each male University level
Football, Kho-Kho and Kabaddi players from North University (Mean Age 23) were volunteered in this study.
Hardy and Nelson (1992) questionnaire was used to measured the self confidence level of the subjects. One way
analysis of variance (ANOVA) was used to find the overall mean significance difference of three groups. List
significance difference (LSD) post-hoc test was used to measure the paired mean significance difference. The
result indicated there was a mean significance difference in self-confidence level between Football, Kho-Kho
and Kabaddi players of North Bengal University. The researcher was concluded that Kho-Kho players are more
confident than the Football and Kabaddi players before attending the respective inter university tournaments.
Key Words: Self-Confidence, Football, Kho-Kho, Kabaddi.
One of the obstacles that hinder the usage of mutation testing is its impracticality, two main contributors of this are a large number of mutants and a large number of test cases involves in the process. Researcher usually tries to address this problem by optimizing the mutants and the test case separately. In this research, we try to tackle both of optimizing mutant and optimizing test-case simultaneousl y using a coevolution optimization method. The coevolution optimization method is chosen for the mutation testing problem because the method works by optimizing multiple collections (population) of a solution. This research found that coevolution is better suited for multiproblem optimization than other single population methods (i.e. Genetic Algorithm), we also propose new indicator to determine the optimal coevolution cycle. The experiment is done to the artificial case, laboratory, and also a real case.
How to optimize with the help of the Particle Swarm Optimization Technique and xlOptimizer ? This brief tutorial will enable you to solve any optimization problem with the application of Particle Swarm Optimization Method. After a brief introduction about the method the tutorial will show you the steps that you will need to follow for application of PSO in optimization even if you do not know any programming.(Some basic knowledge of MS Excel 2010 and later is required).
EVOLUTIONARY COMPUTING TECHNIQUES FOR SOFTWARE EFFORT ESTIMATIONijcsit
Reliable and accurate estimation of software has always been a matter of concern for industry and academia. Numerous estimation models have been proposed by researchers, but no model is suitable for all types of datasets and environments. Since the motive of estimation model is to minimize the gap between actual and estimated effort, the effort estimation process can be viewed as an optimization problem to tune
the parameters. In this paper, evolutionary computing techniques, including, Bee colony optimization, Particle swarm optimization and Ant colony optimization have been employed to tune the parameters of COCOMO Model. The performance of these techniques has been analysed by established performance measure. The results obtained have been validated by using data of Interactive voice response (IVR)
projects. Evolutionary techniques have been found to be more accurate than existing estimation models.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Data Science - Part XIV - Genetic AlgorithmsDerek Kane
This lecture provides an overview on biological evolution and genetic algorithms in a machine learning context. We will start off by going through a broad overview of the biological evolutionary process and then explore how genetic algorithms can be developed that mimic these processes. We will dive into the types of problems that can be solved with genetic algorithms and then we will conclude with a series of practical examples in R which highlights the techniques: The Knapsack Problem, Feature Selection and OLS regression, and constrained optimizations.
A Non-Revisiting Genetic Algorithm for Optimizing Numeric Multi-Dimensional F...ijcsa
Genetic Algorithm (GA) is a robust and popular stochastic optimization algorithm for large and complex search spaces. The major shortcomings of Genetic Algorithms are premature convergence and revisits to individual solutions in the search space. In other words, Genetic algorithm is a revisiting algorithm that escorts to duplicate function evaluations which is a clear wastage of time and computational resources. In this paper, a non-revisiting genetic algorithm with adaptive mutation is proposed for the domain of MultiDimensional numeric function optimization. In this algorithm whenever a revisit occurs, the underlined search point is replaced with a mutated version of the best/random (chosen probabilistically) individual from the GA population. Furthermore, the recommended approach is not using any extra memory resources to avoid revisits. To analyze the influence of the method, the proposed non-revisiting algorithm is evaluated using nine benchmarks functions with two and four dimensions. The performance of the proposed genetic algorithm is superior as contrasted to simple genetic algorithm as confirmed by the experimental results.
Presented at Artificial Intelligence and Machine Learning for Advanced Drug Discovery & Development 2019 on 28th May 2019 by Dr Ed Griffen of MedChemica Ltd
For three decades, many mathematical programming methods have been developed to solve optimization problems. However, until now, there has not been a single totally efficient and robust method to coverall optimization problems that arise in the different engineering fields.Most engineering application design problems involve the choice of design variable values that better describe the behaviour of a system.At the same time, those results should cover the requirements and specifications imposed by the norms for that system. This last condition leads to predicting what the entrance parameter values should be whose design results comply with the norms and also present good performance, which describes the inverse problem.Generally, in design problems the variables are discreet from the mathematical point of view. However, most mathematical optimization applications are focused and developed for continuous variables. Presently, there are many research articles about optimization methods; the typical ones are based on calculus,numerical methods, and random methods.
The calculus-based methods have been intensely studied and are subdivided in two main classes: 1) the direct search methods find a local maximum moving a function over the relative local gradient directions and 2) the indirect methods usually find the local ends solving a set of non-linear equations, resultant of equating the gradient from the object function to zero, i.e., by means of multidimensional generalization of the notion of the function’s extreme points from elementary calculus given smooth function without restrictions to find a possible maximum which is to be restricted to those points whose slope is zero in all directions. The real world has many discontinuities and noisy spaces, which is why it is not surprising that the methods depending upon the restrictive requirements of continuity and existence of a derivative, are unsuitable for all, but a very limited problem domain. A number of schemes have been applied in many forms and sizes. The idea is quite direct inside a finite search space or a discrete infinite search space, where the algorithms can locate the object function values in each space point one at a time. The simplicity of this kind of algorithm is very attractive when the numbers of possibilities are very small. Nevertheless, these outlines are often inefficient, since they do not complete the requirements of robustness in big or highly-dimensional spaces, making it quite a hard task to find the optimal values. Given the shortcomings of the calculus-based techniques and the numerical ones the random methods have increased their popularity.
AUTOMATIC GENERATION AND OPTIMIZATION OF TEST DATA USING HARMONY SEARCH ALGOR...csandit
Software testing is the primary phase, which is performed during software development and it is
carried by a sequence of instructions of test inputs followed by expected output. The Harmony
Search (HS) algorithm is based on the improvisation process of music. In comparison to other
algorithms, the HSA has gain popularity and superiority in the field of evolutionary
computation. When musicians compose the harmony through different possible combinations of
the music, at that time the pitches are stored in the harmony memory and the optimization can
be done by adjusting the input pitches and generate the perfect harmony. The test case
generation process is used to identify test cases with resources and also identifies critical
domain requirements. In this paper, the role of Harmony search meta-heuristic search
technique is analyzed in generating random test data and optimized those test data. Test data
are generated and optimized by applying in a case study i.e. a withdrawal task in Bank ATM
through Harmony search. It is observed that this algorithm generates suitable test cases as well
as test data and gives brief details about the Harmony search method. It is used for test data
generation and optimization
The potential role of ai in the minimisation and mitigation of project delayPieter Rautenbach
Artificial intelligence (AI) can have wide reaching application within the construction
industry, however, the actual application of this set of technologies is currently under exploited. This
paper considers the role that the application of AI can take in optimising the efficiencies of project
execution and how this can potentially reduce project duration and minimise and mitigate delay on
projects.
Application of Genetic Algorithm in Software Engineering: A ReviewIRJESJOURNAL
Abstract. The software engineering is comparatively new and regularly changing field. The big challenge of meeting strict project schedules with high quality software requires that the field of software engineering be automated to large extent and human resource intervention be minimized to optimum level. To achieve this goal the researcher have explored the potential of machine learning approaches as they are adaptable, have learning ability. In this paper, we take a look at how genetic algorithm (GA) can be used to build tool for software development and maintenance tasks.
Comparative Study on Self Confidence among University Level Football, Kho-Kho...IOSR Journals
Abstract: Self-confidence is the belief that one can successfully perform or desired behavior. It is an important
factor to achieve our ultimate goal. Football, Kho-Kho and Kabaddi are very famous game in India involving
various physical fitness components like speed, co-ordination, power, strength, agility etc. The Researcher was
very keen to find out the self-confidence level of North Bengal University Football, Kho-Kho and Kabaddi
players before attending the inter university tournament. The purpose of this study was to compare the selfconfidence
level between Football, Kho-Kho and Kabaddi players. Eighteen each male University level
Football, Kho-Kho and Kabaddi players from North University (Mean Age 23) were volunteered in this study.
Hardy and Nelson (1992) questionnaire was used to measured the self confidence level of the subjects. One way
analysis of variance (ANOVA) was used to find the overall mean significance difference of three groups. List
significance difference (LSD) post-hoc test was used to measure the paired mean significance difference. The
result indicated there was a mean significance difference in self-confidence level between Football, Kho-Kho
and Kabaddi players of North Bengal University. The researcher was concluded that Kho-Kho players are more
confident than the Football and Kabaddi players before attending the respective inter university tournaments.
Key Words: Self-Confidence, Football, Kho-Kho, Kabaddi.
Prediction of Euro 50 Using Back Propagation Neural Network (BPNN) and Geneti...AI Publications
Modeling time series is often associated with the process forecasts certain characteristics in the next period. One of the methods forecasts that developed nowadays is using artificial neural network or more popularly known as a neural network. Use neural network in forecasts time series can be a good solution, but the problem is network architecture and the training method in the right direction. One of the choices that might be using a genetic algorithm. A genetic algorithm is a search algorithm stochastic resonance based on how it works by the mechanisms of natural selection and genetic variation that aims to find a solution to a problem. This algorithm can be used as teaching methods in train models are sent back propagation neural network. The application genetic algorithm and neural network for divination time series aim to get the weight optimum. From the training and testing on the data index share price euro 50 obtained by the RMSE testing 27.8744 and 39.2852 RMSE training. The weight or parameters that produced by has reached an optimum level in second-generation 1000 with the best fitness and the average 0.027771 the fitness of 0.0027847.Model is good to be used to give a prediction that is quite accurate information that is shown by the close target with the output.
Using particle swarm optimization to solve test functions problemsriyaniaes
In this paper the benchmarking functions are used to evaluate and check the particle swarm optimization (PSO) algorithm. However, the functions utilized have two dimension but they selected with different difficulty and with different models. In order to prove capability of PSO, it is compared with genetic algorithm (GA). Hence, the two algorithms are compared in terms of objective functions and the standard deviation. Different runs have been taken to get convincing results and the parameters are chosen properly where the Matlab software is used. Where the suggested algorithm can solve different engineering problems with different dimension and outperform the others in term of accuracy and speed of convergence.
COMPARISON BETWEEN THE GENETIC ALGORITHMS OPTIMIZATION AND PARTICLE SWARM OPT...IAEME Publication
Close range photogrammetry network design is referred to the process of placing a set of
cameras in order to achieve photogrammetric tasks. The main objective of this paper is tried to find
the best location of two/three camera stations. The genetic algorithm optimization and Particle
Swarm Optimization are developed to determine the optimal camera stations for computing the three
dimensional coordinates. In this research, a mathematical model representing the genetic algorithm
optimization and Particle Swarm Optimization for the close range photogrammetry network is
developed. This paper gives also the sequence of the field operations and computational steps for this
task. A test field is included to reinforce the theoretical aspects.
Markov Chain and Adaptive Parameter Selection on Particle Swarm Optimizer ijsc
Particle Swarm Optimizer (PSO) is such a complex stochastic process so that analysis on the stochastic behavior of the PSO is not easy. The choosing of parameters plays an important role since it is critical in the performance of PSO. As far as our investigation is concerned, most of the relevant researches are based on computer simulations and few of them are based on theoretical approach. In this paper, theoretical approach is used to investigate the behavior of PSO. Firstly, a state of PSO is defined in this paper, which contains all the information needed for the future evolution. Then the memory-less property of the state defined in this paper is investigated and proved. Secondly, by using the concept of the state and suitably dividing the whole process of PSO into countable number of stages (levels), a stationary Markov chain is established. Finally, according to the property of a stationary Markov chain, an adaptive method for parameter selection is proposed.
Optimal rule set generation using pso algorithmcsandit
Classification and Prediction is an important resea
rch area of data mining. Construction of
classifier model for any decision system is an impo
rtant job for many data mining applications.
The objective of developing such a classifier is to
classify unlabeled dataset into classes. Here
we have applied a discrete Particle Swarm Optimizat
ion (PSO) algorithm for selecting optimal
classification rule sets from huge number of rules
possibly exist in a dataset. In the proposed
DPSO algorithm, decision matrix approach was used f
or generation of initial possible
classification rules from a dataset. Then the propo
sed algorithm discovers important or
significant rules from all possible classification
rules without sacrificing predictive accuracy.
The proposed algorithm deals with discrete valued d
ata, and its initial population of candidate
solutions contains particles of different sizes. Th
e experiment has been done on the task of
optimal rule selection in the data sets collected f
rom UCI repository. Experimental results show
that the proposed algorithm can automatically evolv
e on average the small number of
conditions per rule and a few rules per rule set, a
nd achieved better classification performance
of predictive accuracy for few classes.
Analysis of Parameter using Fuzzy Genetic Algorithm in E-learning SystemHarshal Jain
The aim of this project is to analyze the parameter, for the inputs to find an optimization problem than the candidate solution we have. This will help us to find more accurate knowledge level of user, using Genetic Algorithm (GA). In this algorithm a population of candidate solutions (called individuals, creatures, or phenotypes) to an optimization problem is evolved toward better solutions.
MARKOV CHAIN AND ADAPTIVE PARAMETER SELECTION ON PARTICLE SWARM OPTIMIZERijsc
Particle Swarm Optimizer (PSO) is such a complex stochastic process so that analysis on the stochastic
behavior of the PSO is not easy. The choosing of parameters plays an important role since it is critical in
the performance of PSO. As far as our investigation is concerned, most of the relevant researches are
based on computer simulations and few of them are based on theoretical approach. In this paper,
theoretical approach is used to investigate the behavior of PSO. Firstly, a state of PSO is defined in this
paper, which contains all the information needed for the future evolution. Then the memory-less property of
the state defined in this paper is investigated and proved. Secondly, by using the concept of the state and
suitably dividing the whole process of PSO into countable number of stages (levels), a stationary Markov
chain is established. Finally, according to the property of a stationary Markov chain, an adaptive method
for parameter selection is proposed.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
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Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
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Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...
M017127578
1. IOSR Journal of Computer Engineering (IOSR-JCE)
e-ISSN: 2278-0661,p-ISSN: 2278-8727, Volume 17, Issue 1, Ver. II (Jan – Feb. 2015), PP 75-78
www.iosrjournals.org
DOI: 10.9790/0661-17127578 www.iosrjournals.org 75 | Page
Application of Genetic Algorithm and Particle Swarm
Optimization in Software Testing
Deepti Arora1
, Anurag Singh Baghel2
1,2,
(Gautam Buddha University,Uttar Pradesh,India)
Abstract: This paper will describe a method for optimizing software testing by finding the most error prone
paths in the program. This can be achieved by a meta-heuristic technique, that is by using genetic algorithm
and particle swarm optimization. As exhaustive software testing is not possible where software is tested with all
the possible inputs , those parts of software are also tested which are not error prone..Goal is to generate test
cases using both the algorithms and then comparative study is done between Particle swarm optimization and
genetic algorithm.
Keywords: Software Testing, Meta-heuristics , Genetic Algorithms, Particle Swarm Optimization, Software test
cases.
I. Introduction
Testing is the one of the crucial phase that is performed during software development. It is a primary
technique which is used to gain consumer confidence in the software. It is conducted by executing the program
developed with test inputs and comparing the observed output with the expected one .[1]Software testing is done
to identify any errors or missing requirements in contrary to the actual requirements. It is done to validate the
quality of the software testing using minimal cost and efforts and to generate high quality test cases. It uncovers
the error in the software, including errors in requirement analysis, design document, coding, system resources
and system environments, hardware problems and their interfaces to the software .[2] It is very time consuming
and laborious activity. It is costly and consumes about 50% of the cost of the software development.[3]. Main
goal of testing to find as many faults as possible in a software. As mentioned earlier it a time consuming activity
,as it is impossible to test the software unit with all the combinations of inputs so there is a need to design
minimum no. of test cases that will discover as many faults as possible . However, software testing automation
is not a straight forward process. For years a lot of research have been done in this area to generate test cases
,researchers developed methods like random test case generator, symbolic test data generators and dynamic test
data generators. This paper applied the EST (Evolutionary software testing ) techniquie.EST is the automatic
software test case generation technique which in turn will reduce the cost of development of software. EST
interprets the task of test case generation as an optimization problem and tries to solve it using a search
technique .EST uses an meta-heuristic search technique that is genetic algorithm to convert the task of test case
generation into an optimal problem. Evolutionary Testing search for optimal test parameter combinations that
satisfy a test criterion. Test criterion is represented fitness function that measures how well optimization
parameters that are automatically generated are satisfying the given test criterion .There are variety of
techniques for test case generation but in this paper we will focus on genetic algorithm and particle swarm
optimization technique.
This paper will present the result of our research in into the application of GA and PSO search
approach, to find optimal solution in the software construct.
II. Genetic Algorithm
Genetic Algorithm (GA) are heuristic search algorithm.[4]They are based on the evolutionary idea of
natural selection and genetics. Genetic algorithm is inspired by the Dalton’s theory about evolution that is
survival of the fittest. Genetic algorithm is a part of evolutionary computing which is a growing field of
Artificial Intelligence. GA exploits the historical information to direct the search in region of better performance
with in the search space. [5] In this way competition among individuals for better resources results in fittest
individual dominating over the weaker ones. Genetic Algorithms are more robust hence they are better than
conventional AI(Artificial Intelligence). GA does not break easily even if there is a slightly change in the input
and it also does not get affected by noise. GA offer benefit over typical search of optimization problem in
searching large state space, multimodal search space and n dimensional surface.
Distinct element in GA is individual and population. Individual represents single solution while
population represents set of Solutions which is currently involved in this search process. Each individual is
represented as chromosome. That is an Initial pool of chromosomes. Population size can be few dozens to
thousands. To do optimization fitness function is required to select the best solution from the population and
2. Application of Genetic Algorithm and Particle Swarm Optimization in Software Testing
DOI: 10.9790/0661-17127578 www.iosrjournals.org 76 | Page
discard those not so good solutions. GA consists of following operators that is Selection Operator chromosomes
are selected for cross-over based on the value of the fitness function Cross- Over Operator combine two
chromosomes to produce new chromosomes. Mutation Operator in this value is randomly changed to create new
genes in the individual.
Pseudo code of GA
Initialize (population)
Evaluate Fitness of the population
While (termination condition is met) do
{
Selection(Population)
Cross-over(Population)
Mutation(population)
Evaluate (Population)
}
GA [6] starts with randomly generated population of individuals or chromosomes .Fitness of individual
is calculated based on some fitness function. After the fitness is calculated selection of individuals is done based
on the Roulette –Wheel Selection method i.e an individual having higher fitness value has the more chance of
getting selected .Then Cross over operator is applied to produce new offspring in the population that may have
better characteristics than their parents. Mutation is done to introduce new individual in the population .its is
done by flipping the bit of the chromosomes.
III. Particle Swarm Optimization
Particle Swarm Optimization (PSO) [7] is a relatively recent heuristic search method. It is similar to
GA in the sense that both are evolutionary algorithms .It is one of the meta-heuristics approach that optimizes a
problem and try to improve candidate solution iteratively. PSO is generally used to solve those problems
whose solution can be represented as a point in an n-dimensional space. In PSO potential solution is called
particle. A number of particles are randomly set into motion through this space. Each particle posses its current
position, current velocity ,and its pbest position .Pbest is the personal best position explored so far. It also
incorporates Gbest that global best position achieved by all its individuals. It is a simple approach and it is
effective across a variety of problem domains.
Pseudo code for PSO
The PSO algorithm consists of just few steps, which are repeated until some stopping condition is
met. The steps are as follow:
Initialize the population of individuals with current position and, velocity.
Evaluate the fitness of the individual particle (Pbest).
Keep track of the individual highest fitness (Gbest).
Modify velocity based on Pbest and Gbest location.
Update the particle position.
PSO starts with initialization of particle velocity and current position. Here particle is in 2-D space .
Fitness value of the particle is calculated according to function. If the fitness of the particle is better than its
previous value update particle x and y position that is its personal best position. Also if the value is better than
gbest position update global best position of the particle. Apply equations to update the x and y velocity vector
of the particles. Process repeats until termination criteria is met or the optimal solution is found.
IV. Implementation
Genetic Algorithm is implemented in c language. result is as follows. GA approach to problem to
maximize the function (1) F(x)=x3
-x2
. X represent 5 digit unsigned binary integer .i.e x can take value [0,31].
Four randomly generated solution to problem are 14,18,24,30. Fitness of the individual is calculated by the
function F(x).String no. 4 has the maximum value having more chances of getting selected. here N is 4 . One
point Crossover is performed in a couple. New offspring is generated. Mutation is performed. We get new
generation Again the process is repeated till the maximum possible value of the solution is obtained .Luckily in
this case in the second iteration we will get desired value. Whereas In most of the cases GA traps in local
optima. But here We have got the global optimum solution.
3. Application of Genetic Algorithm and Particle Swarm Optimization in Software Testing
DOI: 10.9790/0661-17127578 www.iosrjournals.org 77 | Page
Table 1
String
N o.
Initial Population Value of x F(x)=X3
-x2
pi Expected
Count(N*pi)
1 01111 15 3150 0.072 0.289
2 10010 18 5508 0.126 0.505
3 10101 21 8820 0.202 0.809
4 11110 30 26100 0.598 2.395
Sum 43578 1 4
Average 10894 0.25 1
Max 26100 0.598 2.395
Table 2
Before
Crossover
After
Crossover
Mutation
Of element 3 at
position 2
New
Generation is as follows
(Value of x)
01111 01110 01110 14
10010 10011 10011 19
10101 10100 10110 22
11110 11111 11111 31
Particle Swarm Optimization
For Mathematical function (2) val=(x-15)4
-(y-20)2
+9
Particle is initialized with current position and velocity. Program will search for optimal solution
through the movement of particle. Program will keep track of personal best and global best position of the
particle and update its position and velocity .Optimal solution here is to minimize the mathematical function
.PSO is implemented in C language. How the particle moves in the search space to find global best solution is
shown in the Fig 1 & Fig 2. Finally the optimum solution is found when x=15 and y=20.For more complex
problems where it is not possible to easily solve the problem mathematically ,these algorithms can be used.
Figure 1 Movement of particles to find optimum solution
Figure 2 Global best solution
4. Application of Genetic Algorithm and Particle Swarm Optimization in Software Testing
DOI: 10.9790/0661-17127578 www.iosrjournals.org 78 | Page
V. Comparison Of GA And PSO
Genetic Algorithm and Particle Swarm Optimization technique are both evolutionary search algorithm
and have been applied in variety of problem domains.[8 &9] GA can be applied to number of problems like N-
queens problem, Travel salesman problem to find optimal solution. GA suffers from the drawback that it traps in
local optima, that is it does not know how to sacrifice short term fitness for long term fitness. It does not keep in
account the global best position of the individual. This drawback is overcome by Particle Swarm optimization
technique which tracks the particle personal best position as well as global best position ,hence it moves toward
global optima without getting trapped in local optima. GA has been popular because of its parallel nature of
search ,and essentially because it can solve non linear ,multi model problems. It can handle both Continuous and
discrete variables where as PSO can easily handle Continuous variables. In comparison to GA ,PSO has few
parameters and it is easy to implement .Calculation of PSO Algorithm is very simple though it is difficult to
implement with problem of non coordinate system .It is problem dependent, Problem having continuous
variables PSO is superior to GA .Many researchers have compared both the technologies .PSO outperforms
GA, and is more effective in general however superiority of PSO is problem dependent.
VI. Conclusion
PSO is relatively recent heuristic approach, It is similar to Genetic algorithm in a way that they both are
population based evolutionary algorithms. Paper is reported on the application of Genetic Algorithm and
Particle Swarm Optimization. Paper described the basic concepts of GA and PSO ,how the test cases are
generated using genetic algorithm and how they are useful in finding the optimal solution to the problem.
Comparative study is done between both the algorithm where GA can be useful and how PSO overcome the
drawback of GA. We also intend to extend the approach described here so that it is useful to solve many
problems like knapsack problem.
References
[1]. B. Beizer,Software testing techniques, Second Edition, Van Nostrand Reinhold, NewYork, 1990.
[2]. Nirmal Kumar Gupta and Dr. Mukesh Kumar Rohil,, Using Genetic Algorithm for Unit Testing Of Object Oriented Software,
IJSSST, Vol.10, No.3.
[3]. S. Desikan and G. Ramesh, ,Software testing principles & practices, Pearson Education, 2007.
[4]. K. F. Man, ,K. S. Tang, and S. Kwong,,Genetic Algorithms: Concepts and Applications, IEEE Transaction on Industrial Electronics
,VOL. 43, NO. 5, October 1996..
[5]. Abhishek Singhal, Swati Chandna and Abhay Bansal, Optimization of Test Cases Using Genetic Algorithm, International Journal of
Emerging Technology and Advanced Engineering ISSN 2250-2459, Volume 2, Issue 3, March 2012.
[6]. Sanjay Singla, Dharminder Kumar, H M Rai3 and Priti Singla, Hybrid PSO Approach to Automate Test Data Generation for Data
Flow Coverage with Dominance Concepts, International Journal of Advanced Science and Technology Vol. 37, December, 2011
[7]. K. Agrawal and G. Srivastava, Towards software test data generation using discrete quantum particle swarm optimization, ISEC,
Mysore,India, pp. 65-68, February 2010
[8]. Andreas Windisch, Stefan Wappler and Joachim Wegener, Applying Particle Swarm Optimization to Software Testing,
GECCO’07, July 7–11, 2007, London, England, United Kingdom Copyright 2007 ACM 978-1-59593-697-4/07/0007.
[9]. Praveen Ranjan Srivastava1 and Tai-hoon Kim2, Application of Genetic Algorithm in Software Testing,, International Journal of
Software Engineering and Its Applications Vol. 3, No.4, October 2009.