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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.
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.
EFFICIENT FEATURE SUBSET SELECTION MODEL FOR HIGH DIMENSIONAL DATAIJCI JOURNAL
This paper proposes a new method that intends on reducing the size of high dimensional dataset by
identifying and removing irrelevant and redundant features. Dataset reduction is important in the case of
machine learning and data mining. The measure of dependence is used to evaluate the relationship
between feature and target concept and or between features for irrelevant and redundant feature removal.
The proposed work initially removes all the irrelevant features and then a minimum spanning tree of
relevant features is constructed using Prim’s algorithm. Splitting the minimum spanning tree based on the
dependency between features leads to the generation of forests. A representative feature from each of the
forests is taken to form the final feature subset
Optimizing Mobile Robot Path Planning and Navigation by Use of Differential E...IOSR Journals
Abstract: Path planning and navigation is essential for an autonomous robot which can move avoiding the
static obstacles in a real world and to reach the specific target. Optimizing path for the robot movement gives
the optimal distance from the source to the target and save precious time as well. With the development of
various evolutionary algorithms, the differential evolution is taking the pace in comparison to genetic algorithm.
Differential evolution has been deployed quite successfully for solving global optimization problem. Differential
evolution is a very simple yet powerful metaheuristics type problem solving method. In this paper we are
proposing a Differential Evolution based path navigation algorithm for mobile path navigation and analyze its
efficiency with other developed approaches. The proposed algorithm optimized the robot path and navigates the
robot to the proper target efficiently.
Performance Analysis of Genetic Algorithm as a Stochastic Optimization Tool i...paperpublications3
Abstract: Engineering design problems are complex by nature because of their critical objective functions involving many variables and Constraints. Engineers have to ensure the compatibility with the imposed specifications keeping the manufacturing costs low. Moreover, the methodology may vary according to the design problem.
The main issue is to choose the proper tool for optimization. In the earlier days, a design problem was optimized by some of the conventional optimization techniques like gradient Search, evolutionary optimization, random search etc. These are known as classical methods.
The method is to be properly Chosen depending on the nature of the problem- an incorrect choice may sometimes fail to give the optimal solution. So the methods are less robust.
Now-a-days soft-computing techniques are being widely used for optimizing a function. These are more robust. Genetic algorithm is one such method. It is an effective tool in the realm of stochastic optimization (non-classical). The algorithm produces many strings and generation to reach the optimal point.
The main objective of the paper is to optimize engineering design problems using Genetic Algorithm and to analyze how the algorithm reaches the optima effectively and closely. We choose a mathematical expression for the objective function in terms of the design variables and optimize the same under given constraints using GA.
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.
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.
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.
EFFICIENT FEATURE SUBSET SELECTION MODEL FOR HIGH DIMENSIONAL DATAIJCI JOURNAL
This paper proposes a new method that intends on reducing the size of high dimensional dataset by
identifying and removing irrelevant and redundant features. Dataset reduction is important in the case of
machine learning and data mining. The measure of dependence is used to evaluate the relationship
between feature and target concept and or between features for irrelevant and redundant feature removal.
The proposed work initially removes all the irrelevant features and then a minimum spanning tree of
relevant features is constructed using Prim’s algorithm. Splitting the minimum spanning tree based on the
dependency between features leads to the generation of forests. A representative feature from each of the
forests is taken to form the final feature subset
Optimizing Mobile Robot Path Planning and Navigation by Use of Differential E...IOSR Journals
Abstract: Path planning and navigation is essential for an autonomous robot which can move avoiding the
static obstacles in a real world and to reach the specific target. Optimizing path for the robot movement gives
the optimal distance from the source to the target and save precious time as well. With the development of
various evolutionary algorithms, the differential evolution is taking the pace in comparison to genetic algorithm.
Differential evolution has been deployed quite successfully for solving global optimization problem. Differential
evolution is a very simple yet powerful metaheuristics type problem solving method. In this paper we are
proposing a Differential Evolution based path navigation algorithm for mobile path navigation and analyze its
efficiency with other developed approaches. The proposed algorithm optimized the robot path and navigates the
robot to the proper target efficiently.
Performance Analysis of Genetic Algorithm as a Stochastic Optimization Tool i...paperpublications3
Abstract: Engineering design problems are complex by nature because of their critical objective functions involving many variables and Constraints. Engineers have to ensure the compatibility with the imposed specifications keeping the manufacturing costs low. Moreover, the methodology may vary according to the design problem.
The main issue is to choose the proper tool for optimization. In the earlier days, a design problem was optimized by some of the conventional optimization techniques like gradient Search, evolutionary optimization, random search etc. These are known as classical methods.
The method is to be properly Chosen depending on the nature of the problem- an incorrect choice may sometimes fail to give the optimal solution. So the methods are less robust.
Now-a-days soft-computing techniques are being widely used for optimizing a function. These are more robust. Genetic algorithm is one such method. It is an effective tool in the realm of stochastic optimization (non-classical). The algorithm produces many strings and generation to reach the optimal point.
The main objective of the paper is to optimize engineering design problems using Genetic Algorithm and to analyze how the algorithm reaches the optima effectively and closely. We choose a mathematical expression for the objective function in terms of the design variables and optimize the same under given constraints using GA.
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 Feature Subset Selection using Genetic Algorithm for Clusteringidescitation
Feature subset selection is a process of selecting a
subset of minimal, relevant features and is a pre processing
technique for a wide variety of applications. High dimensional
data clustering is a challenging task in data mining. Reduced
set of features helps to make the patterns easier to understand.
Reduced set of features are more significant if they are
application specific. Almost all existing feature subset
selection algorithms are not automatic and are not application
specific. This paper made an attempt to find the feature subset
for optimal clusters while clustering. The proposed Automatic
Feature Subset Selection using Genetic Algorithm (AFSGA)
identifies the required features automatically and reduces
the computational cost in determining good clusters. The
performance of AFSGA is tested using public and synthetic
datasets with varying dimensionality. Experimental results
have shown the improved efficacy of the algorithm with optimal
clusters and computational cost.
Guest Lecture about genetic algorithms in the course ECE657: Computational Intelligence/Intelligent Systems Design, Spring 2016, Electrical and Computer Engineering (ECE) Department, University of Waterloo, Canada.
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.
GA is a search technique that depends on the natural selection and genetics principles and which determines a optimal solution for even a hard issue.genetic algorithm crossover and genetic algorithm for optimization
A HYBRID ALGORITHM BASED ON INVASIVE WEED OPTIMIZATION ALGORITHM AND GREY WOL...gerogepatton
In this research, two algorithms first, considered to be one of hybrid algorithms. And it is algorithm represents invasive weed optimization. This algorithm is a random numerical algorithm and the second algorithm representing the grey wolves optimization. This algorithm is one of the algorithms of swarm intelligence in intelligent optimization. The algorithm of invasive weed optimization is inspired by nature as the weeds have colonial behavior and were introduced by Mehrabian and Lucas in 2006. Invasive weeds are a serious threat to cultivated plants because of their adaptability and are a threat to the overall planting process. The behavior of these weeds has been studied and applied in the invasive weed algorithm. The algorithm of grey wolves, which is considered as a swarm intelligence algorithm, has been used to reach the goal and reach the best solution. The algorithm was designed by SeyedaliMirijalili in 2014 and taking advantage of the intelligence of the squadrons is to avoid falling into local solutions so the new hybridization process between the previous algorithms GWO and IWO and we will symbolize the new algorithm IWOGWO. Comparing the suggested hybrid algorithm with the original algorithms it results were excellent. The optimum solution was found in most of test functions.
A HYBRID ALGORITHM BASED ON INVASIVE WEED OPTIMIZATION ALGORITHM AND GREY WOL...ijaia
In this research, two algorithms first, considered to be one of hybrid algorithms. And it is algorithm represents invasive weed optimization. This algorithm is a random numerical algorithm and the second algorithm representing the grey wolves optimization. This algorithm is one of the algorithms of swarm intelligence in intelligent optimization. The algorithm of invasive weed optimization is inspired by nature as the weeds have colonial behavior and were introduced by Mehrabian and Lucas in 2006. Invasive weeds are a serious threat to cultivated plants because of their adaptability and are a threat to the overall planting process. The behavior of these weeds has been studied and applied in the invasive weed algorithm. The algorithm of grey wolves, which is considered as a swarm intelligence algorithm, has been used to reach the goal and reach the best solution. The algorithm was designed by SeyedaliMirijalili in 2014 and taking advantage of the intelligence of the squadrons is to avoid falling into local solutions so the new hybridization process between the previous algorithms GWO and IWO and we will symbolize the new algorithm IWOGWO.Comparing the suggested hybrid algorithm with the original algorithms it results were excellent. The optimum solution was found in most of test functions.
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.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
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.
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.
El Xstat consiste en una jeringa con esponjas estériles en su interior que, al momento de hacer contacto con la sangre la absorbe y detiene la hemorragia.
Automatic Feature Subset Selection using Genetic Algorithm for Clusteringidescitation
Feature subset selection is a process of selecting a
subset of minimal, relevant features and is a pre processing
technique for a wide variety of applications. High dimensional
data clustering is a challenging task in data mining. Reduced
set of features helps to make the patterns easier to understand.
Reduced set of features are more significant if they are
application specific. Almost all existing feature subset
selection algorithms are not automatic and are not application
specific. This paper made an attempt to find the feature subset
for optimal clusters while clustering. The proposed Automatic
Feature Subset Selection using Genetic Algorithm (AFSGA)
identifies the required features automatically and reduces
the computational cost in determining good clusters. The
performance of AFSGA is tested using public and synthetic
datasets with varying dimensionality. Experimental results
have shown the improved efficacy of the algorithm with optimal
clusters and computational cost.
Guest Lecture about genetic algorithms in the course ECE657: Computational Intelligence/Intelligent Systems Design, Spring 2016, Electrical and Computer Engineering (ECE) Department, University of Waterloo, Canada.
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.
GA is a search technique that depends on the natural selection and genetics principles and which determines a optimal solution for even a hard issue.genetic algorithm crossover and genetic algorithm for optimization
A HYBRID ALGORITHM BASED ON INVASIVE WEED OPTIMIZATION ALGORITHM AND GREY WOL...gerogepatton
In this research, two algorithms first, considered to be one of hybrid algorithms. And it is algorithm represents invasive weed optimization. This algorithm is a random numerical algorithm and the second algorithm representing the grey wolves optimization. This algorithm is one of the algorithms of swarm intelligence in intelligent optimization. The algorithm of invasive weed optimization is inspired by nature as the weeds have colonial behavior and were introduced by Mehrabian and Lucas in 2006. Invasive weeds are a serious threat to cultivated plants because of their adaptability and are a threat to the overall planting process. The behavior of these weeds has been studied and applied in the invasive weed algorithm. The algorithm of grey wolves, which is considered as a swarm intelligence algorithm, has been used to reach the goal and reach the best solution. The algorithm was designed by SeyedaliMirijalili in 2014 and taking advantage of the intelligence of the squadrons is to avoid falling into local solutions so the new hybridization process between the previous algorithms GWO and IWO and we will symbolize the new algorithm IWOGWO. Comparing the suggested hybrid algorithm with the original algorithms it results were excellent. The optimum solution was found in most of test functions.
A HYBRID ALGORITHM BASED ON INVASIVE WEED OPTIMIZATION ALGORITHM AND GREY WOL...ijaia
In this research, two algorithms first, considered to be one of hybrid algorithms. And it is algorithm represents invasive weed optimization. This algorithm is a random numerical algorithm and the second algorithm representing the grey wolves optimization. This algorithm is one of the algorithms of swarm intelligence in intelligent optimization. The algorithm of invasive weed optimization is inspired by nature as the weeds have colonial behavior and were introduced by Mehrabian and Lucas in 2006. Invasive weeds are a serious threat to cultivated plants because of their adaptability and are a threat to the overall planting process. The behavior of these weeds has been studied and applied in the invasive weed algorithm. The algorithm of grey wolves, which is considered as a swarm intelligence algorithm, has been used to reach the goal and reach the best solution. The algorithm was designed by SeyedaliMirijalili in 2014 and taking advantage of the intelligence of the squadrons is to avoid falling into local solutions so the new hybridization process between the previous algorithms GWO and IWO and we will symbolize the new algorithm IWOGWO.Comparing the suggested hybrid algorithm with the original algorithms it results were excellent. The optimum solution was found in most of test functions.
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.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
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.
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.
El Xstat consiste en una jeringa con esponjas estériles en su interior que, al momento de hacer contacto con la sangre la absorbe y detiene la hemorragia.
Dynamic Detection of Malicious BehaviorEndgameInc
See a brief overview of five of the most common malicious behavior strategies, the changes in the threat landscape based on these strategies, and examples of dynamic detection for malicious behavior.
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.
Optimized Robot Path Planning Using Parallel Genetic Algorithm Based on Visib...IJERA Editor
An analysis is made for optimized path planning for mobile robot by using parallel genetic algorithm. The
parallel genetic algorithm (PGA) is applied on the visible midpoint approach to find shortest path for mobile
robot. The hybrid ofthese two algorithms provides a better optimized solution for smooth and shortest path for
mobile robot. In this problem, the visible midpoint approach is used to make the effectiveness for avoiding
local minima. It gives the optimum paths which are always consisting on free trajectories. But the
proposedhybrid parallel genetic algorithm converges very fast to obtain the shortest route from source to
destination due to the sharing of population. The total population is partitioned into a number subgroups to
perform the parallel GA. The master thread is the center of information exchange and making selection with
fitness evaluation.The cell to cell crossover makes the algorithm significantly good. The problem converges
quickly with in a less number of iteration.
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.
Survey on evolutionary computation tech techniques and its application in dif...ijitjournal
In computer science, 'evolutionary computation' is an algorithmic tool based on evolution. It implements
random variation, reproduction and selection by altering and moving data within a computer. It helps in
building, applying and studying algorithms based on the Darwinian principles of natural selection. In this
paper, studies about different evolutionary computation techniques used in some applications specifically
image processing, cloud computing and grid computing is carried out briefly. This work is an effort to help
researchers from different fields to have knowledge on the techniques of evolutionary computation
applicable in the above mentioned areas.
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
Feature selection in high-dimensional datasets is
considered to be a complex and time-consuming problem. To
enhance the accuracy of classification and reduce the execution
time, Parallel Evolutionary Algorithms (PEAs) can be used. In
this paper, we make a review for the most recent works which
handle the use of PEAs for feature selection in large datasets.
We have classified the algorithms in these papers into four main
classes (Genetic Algorithms (GA), Particle Swarm Optimization
(PSO), Scattered Search (SS), and Ant Colony Optimization
(ACO)). The accuracy is adopted as a measure to compare the
efficiency of these PEAs. It is noticeable that the Parallel Genetic
Algorithms (PGAs) are the most suitable algorithms for feature
selection in large datasets; since they achieve the highest accuracy.
On the other hand, we found that the Parallel ACO is timeconsuming
and less accurate comparing with other PEA.
Parallel and distributed genetic algorithm with multiple objectives to impro...khalil IBRAHIM
we argue that the timetabling problem reflects the problem of scheduling university courses, So you must specify the range of time periods and a group of instructors for a range of lectures to check a set of constraints and reduce the cost of other constraints ,this is the problem called NP-hard, it is a class of problems that are informally, it’s mean that necessary operations to solve the problem will increase exponentially and directly proportional to the size of the problem, The construction of timetable is the most complicated problem that was facing many universities, and increased by size of the university data and overlapping disciplines between colleges, and when a traditional algorithm (EA) is unable to provide satisfactory results, a distributed EA (dEA), which deploys the population on distributed systems, it also offers an opportunity to solve extremely high dimensional problems through distributed coevolution using a divide-and-conquer mechanism, Further, the distributed environment allows a dEA to maintain population diversity, thereby avoiding local optima and also facilitating multi-objective search, by employing different distribution models to parallelize the processing of EAs, we designed a genetic algorithm suitable for Universities environment and the constraints facing it when building timetable for lectures.
Nature Inspired Models And The Semantic WebStefan Ceriu
In this paper we present a series of nature inspired models used as alternative solutions for Semantic Web concerns. Some of the methods presented in this article perform better than classic algorithms by enhancing response time and computational costs. Others are just proof of concept, first steps towards new techniques that will improve their respective field. The intricate nature of the Semantic Web urges the need for faster, more intelligent algorithms and nature inspired models have been proven to be more than suitable for such complex tasks.
In recent years, consumers and legislation have been pushing companies to optimize their activities in such a way as to reduce negative environmental and social impacts more and more. In the other side, companies
must keep their total supply chain costs as low as possible to remain competitive.This work aims to develop a model to traveling salesman problem including environmental impacts and to identify, as far as possible, the contribution of genetic operator’s tuning and setting in the success and
efficiency of genetic algorithms for solving this problem with consideration of CO2 emission due to transport. This efficiency is calculated in terms of CPU time consumption and convergence of the solution. The best transportation policy is determined by finding a balance between financial and environmental
criteria.Empirically, we have demonstrated that the performance of the genetic algorithm undergo relevant
improvements during some combinations of parameters and operators which we present in our results part.
In real world applications, most of the optimization problems involve more than one objective to
be optimized. The objectives in most of engineering problems are often conflicting, i.e., maximize
performance, minimize cost, maximize reliability, etc. In the case, one extreme solution would not satisfy
both objective functions and the optimal solution of one objective will not necessary be the best solution
for other objective(s). Therefore different solutions will produce trade-offs between different objectives
and a set of solutions is required to represent the optimal solutions of all objectives. Multi-objective
formulations are realistic models for many complex engineering optimization problems. Customized
genetic algorithms have been demonstrated to be particularly effective to determine excellent solutions to
these problems. A reasonable solution to a multi-objective problem is to investigate a set of solutions, each
of which satisfies the objectives at an acceptable level without being dominated by any other solution. In
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JMeter webinar - integration with InfluxDB and Grafana
E034023028
1. International Journal of Computational Engineering Research||Vol, 03||Issue, 4||
www.ijceronline.com ||April||2013|| Page 23
Path Planning Optimization Using Genetic Algorithm – A
Literature Review
1,
Er. Waghoo Parvez , 2,
Er. Sonal Dhar
1,
(Department of Mechanical Engg , Mumbai University, MHSSCOE , Mumbai - 400008.
2,
(Department of Production Engg, Mumbai University, DJSCOE, Mumbai – 410206.
I. INTRODUCTION
Motion planning is a term used in robotics for the process of detailing a task into discrete motions. It is
a process to compute a collision-free path between the initial and final configuration for a rigid or articulated
object (the "robot") among obstacles. It is aimed at enabling robots with capabilities of automatically deciding
and executing a sequence motion in order to achieve a task without collision with other objects in a given
environment. Typically the obstacles and the mobile objects are modeled. Given a source position & orientation
for mobile object and goal position & orientation, a search is made for a path from source to goal that is
collision free and perhaps satisfied additional criteria such as a short path, a path which can be found quickly or
a path which does not wander too close to any one of the obstacles. The general path planning problem requires
a search in six dimensional spaces since the mobile object can have three translational and three rotational
degrees of freedom. But still there are three dimensional search problems which have two translational and one
rotational degrees of freedom. [10]The Genetic algorithm is an adaptive heuristic search method based on
population genetics. Genetic algorithm were introduced by John Holland in the early 1970s [1].Genetic
algorithm is a probabilistic search algorithm based on the mechanics of natural selection and natural genetics.
Genetic algorithm is started with a set of solutions called population. A solution is represented by a
chromosome. The population size is preserved throughout each generation. At each generation, fitness of each
chromosome is evaluated, and then chromosomes for the next generation are probabilistically selected according
to their fitness values. Some of the selected chromosomes randomly mate and produce offspring. When
producing offspring, crossover and mutation randomly occurs. Because chromosomes with high fitness values
have high probability of being selected, chromosomes of the new generation may have higher average fitness
value than those of the old generation. The process of evolution is repeated until the end condition is satisfied.
The solutions in genetic algorithms are called chromosomes or strings [2].
A genetic algorithm is a search technique used in computing to find exact or approximate solutions to
optimization and search problems. Genetic algorithms are categorized as global search heuristics. Genetic
algorithms are a particular class of evolutionary algorithms (EA) that use techniques inspired by evolutionary
biology such as inheritance, mutation, selection, and crossover [7]. Genetic algorithms have been used to find
optimal solutions to complex problems in various domains such as biology, engineering, computer science, and
social science. Genetic algorithms fall under the heading of evolutionary algorithm. Evolutionary algorithms are
used to solve problems that do not already have a well defined efficient solution. Genetic algorithm have been
used to solve optimization problems (scheduling, shortest path, etc), and in modeling systems where randomness
is involved (e.g., the stock market).
Abstract
This paper presents a review to the path planning optimization problem using genetic
algorithm as a tool. Path planning is a term used in robotics for the process of detailing a task into
discrete motions. It is aimed at enabling robots with capabilities of automatically deciding and
executing a sequence motion in order to achieve a task without collision with other objects in a given
environment. Genetic algorithms are considered as a search process used in computing to find exact or
an approximate solution for optimization and search problems. There are also termed as global search
heuristics. These techniques are inspired by evolutionary biology such as inheritance mutation,
selection and cross over.
Keywords - Chromosome, Genetic Algorithm (GA) , Mutation, Optimization, Path Planning.
2. Path Planning Optimization Using Genetic…
www.ijceronline.com ||April||2013|| Page 24
II. GENETIC ALGORITHM
2.1. Initialization
Initially many individual solutions are randomly generated to form an initial population. The
population size depends on the nature of the problem, but typically contains several hundreds or thousands of
possible solutions. Traditionally, the population is generated randomly, covering the entire range of possible
solutions (the search space).
Figure No.1 Flowchart of GA [9]
2.2. Selection
During each successive generation, a proportion of the existing population is selected to breed a new
generation. Individual solutions are selected through a fitness-based process, where fitter solutions (as measured
by a fitness function) are typically more likely to be selected. Certain selection methods rate the fitness of each
solution and preferentially select the best solutions.
Most functions are stochastic and designed so that a small proportion of less fit solutions are selected. This
helps keep the diversity of the population large, preventing premature convergence on poor solutions. Popular
and well-studied selection methods include roulette wheel selection and tournament selection.
2.3. Reproduction
The next step is to generate a second generation population of solutions from those selected through
genetic operators: crossover (also called recombination), and/or mutation. For each new solution to be produced,
a pair of “parent” solutions is selected for breeding from the pool selected previously.
By producing a “child” solution using the above methods of crossover and mutation, a new solution is
created which typically shares many of the characteristics of its “parents”. New parents are selected for each
new child, and the process continues until a new population of solutions of appropriate size is generated.
These processes ultimately result in the next generation population of chromosomes that is different
from the initial generation. Generally the average fitness will have increased by this procedure for the
population, since only the best or genetic algorithm from the first generation are selected for breeding, along
with a small proportion of less fit solutions.
3. Path Planning Optimization Using Genetic…
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2.4. Termination
This generational process is repeated until a termination condition has been reached. Common
terminating conditions are:
A solution is found that satisfies minimum criteria;
Fixed number of generations reached;
Allocated budget (computation time/money) reached;
The highest ranking solution’s fitness is reaching or has reached a plateau such that successive iterations
no longer produce better results;
Manual inspection.
2.5 APPLICATIONS OF GENETIC ALGORITHM [8]
Nonlinear dynamical systems–predicting, data analysis
Robot trajectory planning
Evolving LISP programs (genetic programming)
Strategy planning
Finding shape of protein molecules
TSP and sequence scheduling
Functions for creating images
Control–gas pipeline, pole balancing, missile evasion, pursuit
Design–semiconductor layout, aircraft design, keyboard configuration, communication networks
Scheduling–manufacturing, facility scheduling, resource allocation
Machine Learning–Designing neural networks, both architecture and weights.
Signal Processing–filter design
III. LITERATURE REVIEW
3.1 Path planning in construction sites: performance evaluation of the Dijkstra, A*
, and GA search
algorithms A.R. Soltani, H. Tawfik, J.Y. Goulermas, T. Fernando [2] says:
The study illustrated the potential of deterministic and probabilistic search algorithms in addressing the
site path planning issues with multiple objectives. The application generate the shortest path, low risk path,
most visible path, and finally the path that reflects a combination of low risks, short distance, and high visibility
between two site locations. Dijkstra algorithm can find optimal solutions to problems by systematically
generating path nodes and testing them against a goal, but becoming inefficient for large-scale problems. A*
can
find optimal and near to optimal solutions more efficiently by directing search towards the goal by means of
heuristic functions, reducing the time complexity substantially. These algorithms suffer from the curse of
dimensionality effect, which limits the Dijkstra and A*
operation to small and medium problems.
Figure No.2 Courtesy [2] environment for research
4. Path Planning Optimization Using Genetic…
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Probabilistic optimization approach based on GA generates a set of feasible, optimal, and close-to-optimal
solutions that captures globally optimal solutions. GA operators exploit the similarities in string structures to
make an effective search. Good regions of the search space get exponentially more copies and get combined
with each other by the action of GA operators and finally form the optimum or a near-optimum solution in
substantially less time. The GA’s performance limitations are mainly related to obtaining less accurate solutions
and the time-consuming fine-tuning process to guide the search. The future avenues for this work include
investigation of the applicability of fuzzy based multi-criteria evaluation, and hybrid optimization search
algorithms.
3.2 Dynamic path planning of mobile robots with improved genetic algorithm Adem Tuncer , Mehmet
Yildirim [3] says:
They have improved a new mutation operator for the GA and applied it to the path planning problem of
mobile robots. The improved mutation method simultaneously checks all the free nodes close to mutation node
instead of randomly selecting a node one by one. The method accepts the node according to the fitness value of
total path instead of the direction of movement through the mutated node. It is clearly seen from the results that
the GA with the proposed mutation operator can find the optimal path far too many times than the other methods
do. The average fitness values and the average generation numbers of the proposed method are better than the
other methods.
Figure No.3 Courtesy [3] environment for research
3.3 Multi-robot path planning using co-evolutionary genetic programming Rahul Kala [4] says:
Motion planning for multiple mobile robots must ensure the optimality of the path of each and every
robot, as well as overall path optimality, which requires cooperation amongst robots. The paper proposes a
solution to the problem, considering different source and goal of each robot. Each robot uses genetic
programming for figuring the optimal path in a maze-like map, while a master evolutionary algorithm caters to
the needs of overall path optimality. Co-operation amongst the individual robots, evolutionary algorithms
ensures generation of overall optimal paths. Experiments are carried out with a number of maps, scenarios, and
different speeds. Experimental results confirm the usefulness of the algorithm in a variety of scenarios.The
modeling scenario has a maze like map where the different robots are initially located at distinct places and are
given their own goals that they are supposed to reach. It further assumes that each robot moves with its own
5. Path Planning Optimization Using Genetic…
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speed. The algorithm makes use of co evolutionary genetic programming. At the first level a linear
representation of genetic programming is used. The individual in this case consists of instructions for movement
whenever a cross is encountered. The other level consists of a genetic algorithm instance. This algorithm selects
the individuals from the genetic programming and tries to generate a combination such that the overall path of
all the robots combined is optimal.
Figure No.4 Courtesy [4] environment for research
3.4 A new vibrational genetic algorithm enhanced with a Voronoi diagram for path Planning of
autonomous UAV Y. Volkan Pehlivanoglu [5] says:
The algorithm emphasizes a new mutation application strategy and diversity variety such as the global
random and the local random diversity. Clustering method and Voronoi diagram concepts are used within the
initial population phase of mVGA process. The new algorithm and three additional GA’s in the paper are
applied to the path planning problem in two different three-dimensional (3D) environments such as sinusoidal
and city type terrain models and their results are compared. The first mutation operator is applied to all genes of
the whole population and this application provided global but random diversity in the population. The global
diversity afforded a chance for the population to escape from all local optima. The second mutation operator
was specifically applied to the genes of an elite individual in the population. This application provided local
diversity leading to a fast convergence. From the results obtained, it is concluded that a Voronoi supported multi
frequency vibrational genetic algorithm is an efficient and fast algorithm since it avoided all local optima within
relatively short optimization cycles.
3.5 Path planning on a cuboid using genetic algorithms Aybars UG˘UR [6] says:
Optimization on a cuboid has potential applications for areas like path planning on the faces of
buildings, rooms, furniture, books, and products or simulating the behaviors of insects. This paper, addresses a
variant of the TSP in which all points (cities) and paths (solution) are on the faces of a cuboid. They developed
an effective hybrid method based on genetic algorithms and 2-opt to adapt the Euclidean TSP to the surface of a
cuboid. The aim is to develop a simple and efficient method to find the optimum route visiting all items on a
cuboid, one of the most common man-made object shapes. They selected a good TSP solving hybrid method
based on GA and 2-opt that has been used for many years. They integrated it with an algorithm developed to
calculate the distances of any two points on a cuboid; this implementation allows the hybrid method to be
replaced by faster TSP solvers. In accordance with the main goal of this study, the first TSP optimization results
were obtained and presented for different point densities in the cuboid environment. A second contribution is the
presentation approach of the solution and environment. The user can rotate or scale the cuboid and trace the
6. Path Planning Optimization Using Genetic…
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optimum path easily.
Figure No.5 Courtesy [6] environment for research
IV. CONCLUSION
Based on the various papers studied and textbooks referred it can be concluded that for multi objective
optimisation problems genetic algorithms find variety of applications in different fields. Optimal path planning
is one of the important factors in scheduling, moving, transporting, etc.Various factors considered for optimal
path planning includes avoiding obstacles, minimum path finding, multi objectives constraining, safe distance
travelling, etc. For finding the optimal path various optimization techniques are used out of which genetic
algorithm finds extreme applications as it gives optimum results considering global population.If proper
mathematical model is formed it can give the optimum results in optimum time for path planning/motion
planning in the field of robotics.
REFERENCES
[1] Manoj Kumar, Mohammad Husian, Naveen Upreti, & Deepti Gupta “Genetic Algorithm: Review & Application” International
Journal of Information Technology and Knowledge Management July-December 2010, Volume 2, No. 2, pp. 451-454.
[2] A. R Soltani, H Tawfik, et al” Path planning in construction sites: performance evaluation of the Dijkstra, A* and GA search
Algorithm”, Advanced Engineering Informatics pg 291-303.
[3] Adem Tuncer, Mehmet Yildirim ”Dynamic path planning of mobile robots with improved genetic algorithm” Computers &
Electrical Engineering submitted for publication.
[4] Rahul Kala “Multi-robot path planning using co-evolutionary genetic programming” Expert Systems with Applications 39
(2012) 3817–3831.
[5] Y. Volkan Pehlivanoglu “A new vibrational genetic algorithm enhanced with a Voronoi diagram for path planning of
autonomous UAV” Aerospace Science and Technology 16 (2012) 47–55.
[6] Aybars UG˘UR “Path planning on a cuboid using genetic algorithms” Information Sciences 178 (2008) 3275–3287.
Books:
[7] David E. Goldberg , Genetic Algorithms in search, optimization & Machine Learning“ (Pearson Education Twelfth Impression
2013).
[8] Melanie Mitchell, An Introduction to Genetic Algorithms (Prentice Hall of India Edition 2005).
[9] S.N.Sivanandam S.N.Deepa Introduction to Genetic Algorithms (Springer Edition 2008)
Website:
[10] http://en.wikipedia.org/wiki/Motion_