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.
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.
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 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
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.
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.
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 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
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.
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.
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.
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
Preparation of courses at every university is done by hand. This method has limitations that often cause collisions schedule. In lectures and lab scheduling frequent collision against the faculty member teaching schedule, collisions on the class schedule and student, college collision course with lab time, the allocation of the use of the rooms were not optimal. Heuristic method of genetic algorithm based on the mechanism of natural selection; it is a process of biological evolution. Genetic algorithms are used to obtain optimal schedule that consists of the initialization process of the population, fitness evaluation, selection, crossover, and mutation. Data used include the teaching of data, the data subjects, the room data and time data retrieved from the database of the Faculty of Computer Science, Universitas Pembangunan Panca Budi. The data in advance through the stages of the process of genetic algorithms to get optimal results The results of this study in the form of a schedule of courses has been optimized so that no error occurred and gaps.
Integrated bio-search approaches with multi-objective algorithms for optimiza...TELKOMNIKA JOURNAL
Optimal selection of features is very difficult and crucial to achieve, particularly for the task of classification. It is due to the traditional method of selecting features that function independently and generated the collection of irrelevant features, which therefore affects the quality of the accuracy of the classification. The goal of this paper is to leverage the potential of bio-inspired search algorithms, together with wrapper, in optimizing multi-objective algorithms, namely ENORA and NSGA-II to generate an optimal set of features. The main steps are to idealize the combination of ENORA and NSGA-II with suitable bio-search algorithms where multiple subset generation has been implemented. The next step is to validate the optimum feature set by conducting a subset evaluation. Eight (8) comparison datasets of various sizes have been deliberately selected to be checked. Results shown that the ideal combination of multi-objective algorithms, namely ENORA and NSGA-II, with the selected bio-inspired search algorithm is promising to achieve a better optimal solution (i.e. a best features with higher classification accuracy) for the selected datasets. This discovery implies that the ability of bio-inspired wrapper/filtered system algorithms will boost the efficiency of ENORA and NSGA-II for the task of selecting and classifying features.
A Novel Hybrid Voter Using Genetic Algorithm and Performance HistoryWaqas Tariq
Triple Modular Redundancy (TMR) is generally used to increase the reliability of real time systems where three similar modules are used in parallel and the final output is arrived at using voting methods. Numerous majority voting techniques have been proposed in literature however their performances are compromised for some typical set of module output value. Here we propose a new voting scheme for analog systems retaining the advantages of previous reported schemes and reduce the disadvantages associated with them. The scheme utilizes a genetic algorithm and previous performances history of the modules to calculate the final output. The scheme has been simulated using MATLAB and the performance of the voter has been compared with that of fuzzy voter proposed by Shabgahi et al [4]. The performance of the voter proposed here is better than the existing voters.
A fast clustering based feature subset selection algorithm for high-dimension...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
SURVEY ON MODELLING METHODS APPLICABLE TO GENE REGULATORY NETWORKijbbjournal
Gene Regulatory Network (GRN) plays an important role in knowing insight of cellular life cycle. It gives
information about at which different environmental conditions genes of particular interest get over
expressed or under expressed. Modelling of GRN is nothing but finding interactive relationships between
genes. Interaction can be positive or negative. For inference of GRN, time series data provided by
Microarray technology is used. Key factors to be considered while constructing GRN are scalability,
robustness, reliability and maximum detection of true positive interactions between genes. This paper
gives detailed technical review of existing methods applied for building of GRN along with scope for
future work.
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.
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.
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.
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
Preparation of courses at every university is done by hand. This method has limitations that often cause collisions schedule. In lectures and lab scheduling frequent collision against the faculty member teaching schedule, collisions on the class schedule and student, college collision course with lab time, the allocation of the use of the rooms were not optimal. Heuristic method of genetic algorithm based on the mechanism of natural selection; it is a process of biological evolution. Genetic algorithms are used to obtain optimal schedule that consists of the initialization process of the population, fitness evaluation, selection, crossover, and mutation. Data used include the teaching of data, the data subjects, the room data and time data retrieved from the database of the Faculty of Computer Science, Universitas Pembangunan Panca Budi. The data in advance through the stages of the process of genetic algorithms to get optimal results The results of this study in the form of a schedule of courses has been optimized so that no error occurred and gaps.
Integrated bio-search approaches with multi-objective algorithms for optimiza...TELKOMNIKA JOURNAL
Optimal selection of features is very difficult and crucial to achieve, particularly for the task of classification. It is due to the traditional method of selecting features that function independently and generated the collection of irrelevant features, which therefore affects the quality of the accuracy of the classification. The goal of this paper is to leverage the potential of bio-inspired search algorithms, together with wrapper, in optimizing multi-objective algorithms, namely ENORA and NSGA-II to generate an optimal set of features. The main steps are to idealize the combination of ENORA and NSGA-II with suitable bio-search algorithms where multiple subset generation has been implemented. The next step is to validate the optimum feature set by conducting a subset evaluation. Eight (8) comparison datasets of various sizes have been deliberately selected to be checked. Results shown that the ideal combination of multi-objective algorithms, namely ENORA and NSGA-II, with the selected bio-inspired search algorithm is promising to achieve a better optimal solution (i.e. a best features with higher classification accuracy) for the selected datasets. This discovery implies that the ability of bio-inspired wrapper/filtered system algorithms will boost the efficiency of ENORA and NSGA-II for the task of selecting and classifying features.
A Novel Hybrid Voter Using Genetic Algorithm and Performance HistoryWaqas Tariq
Triple Modular Redundancy (TMR) is generally used to increase the reliability of real time systems where three similar modules are used in parallel and the final output is arrived at using voting methods. Numerous majority voting techniques have been proposed in literature however their performances are compromised for some typical set of module output value. Here we propose a new voting scheme for analog systems retaining the advantages of previous reported schemes and reduce the disadvantages associated with them. The scheme utilizes a genetic algorithm and previous performances history of the modules to calculate the final output. The scheme has been simulated using MATLAB and the performance of the voter has been compared with that of fuzzy voter proposed by Shabgahi et al [4]. The performance of the voter proposed here is better than the existing voters.
A fast clustering based feature subset selection algorithm for high-dimension...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
SURVEY ON MODELLING METHODS APPLICABLE TO GENE REGULATORY NETWORKijbbjournal
Gene Regulatory Network (GRN) plays an important role in knowing insight of cellular life cycle. It gives
information about at which different environmental conditions genes of particular interest get over
expressed or under expressed. Modelling of GRN is nothing but finding interactive relationships between
genes. Interaction can be positive or negative. For inference of GRN, time series data provided by
Microarray technology is used. Key factors to be considered while constructing GRN are scalability,
robustness, reliability and maximum detection of true positive interactions between genes. This paper
gives detailed technical review of existing methods applied for building of GRN along with scope for
future work.
In this Machine Learning tutorial, we will cover the top Neural Network Algorithms. These algorithms are used to train the Artificial Neural Network. This blog provides you a deep learning of the Gradient Descent, Evolutionary Algorithms, and Genetic Algorithm in Neural Network.
Evolutionary Computing is a research area within computer science. As the name suggest, it is a special flavour of computing, which draws inspiration from the process of natural evolution. The fundamental metaphor of evolutionary computing relates this powerful natural evolution to a particular style of problem solving – that of trial and error.
COMPARATIVE STUDY OF DIFFERENT ALGORITHMS TO SOLVE N QUEENS PROBLEMijfcstjournal
This Paper provides a brief description of the Genetic Algorithm (GA), the Simulated Annealing (SA)
Algorithm, the Backtracking (BT) Algorithm and the Brute Force (BF) Search Algorithm and attempts to
explain the way as how the Proposed Genetic Algorithm (GA), the Proposed Simulated Annealing (SA)
Algorithm using GA, the Backtracking (BT) Algorithm and the Brute Force (BF) Search Algorithm can be
employed in finding the best solution of N Queens Problem and also, makes a comparison between these
four algorithms. It is entirely a review based work. The four algorithms were written as well as
implemented. From the Results, it was found that, the Proposed Genetic Algorithm (GA) performed better
than the Proposed Simulated Annealing (SA) Algorithm using GA, the Backtracking (BT) Algorithm and
the Brute Force (BF) Search Algorithm and it also provided better fitness value (solution) than the
Proposed Simulated Annealing Algorithm (SA) using GA, the Backtracking (BT) Algorithm and the Brute
Force (BF) Search Algorithm, for different N values. Also, it was noticed that, the Proposed GA took more
time to provide result than the Proposed SA using GA.
Multi-Population Methods with Adaptive Mutation for Multi-Modal Optimization ...ijscai
This paper presents an efficient scheme to locate multiple peaks on multi-modal optimization problems by
using genetic algorithms (GAs). The premature convergence problem shows due to the loss of diversity,
the multi-population technique can be applied to maintain the diversity in the population and the
convergence capacity of GAs. The proposed scheme is the combination of multi-population with adaptive
mutation operator, which determines two different mutation probabilities for different sites of the
solutions. The probabilities are updated by the fitness and distribution of solutions in the search space
during the evolution process. The experimental results demonstrate the performance of the proposed
algorithm based on a set of benchmark problems in comparison with relevant algorithms.
Genetic Algorithm (GA) is a search-based optimization technique based on the principles of Genetics and Natural Selection. It is frequently used to find optimal or near-optimal solutions to difficult problems which otherwise would take a lifetime to solve. It is frequently used to solve optimization problems, in research, and in machine learning.
Comparative study of different algorithmsijfcstjournal
This Paper provides a brief description of the Genetic Algorithm (GA), the Simulated Annealing (SA) Algorithm, the Backtracking (BT) Algorithm and the Brute Force (BF) Search Algorithm and attempts to explain the way as how our Proposed Genetic Algorithm (GA), Proposed Simulated Annealing (SA) Algorithm using GA, Classical Backtracking (BT) Algorithm and Classical Brute Force (BF) Search Algorithm can be employed in finding the best solution of N Queens Problem and also, makes a comparison between these four algorithms. It is entirely a review based work. The four algorithms were written as well as implemented. From the Results, it was found that, the Proposed Genetic Algorithm (GA) performed better than the Proposed Simulated Annealing (SA) Algorithm using GA, the Backtracking (BT) Algorithm and the Brute Force (BF) Search Algorithm and it also provided better fitness value (solution) than the Proposed Simulated Annealing Algorithm (SA) using GA, the Backtracking (BT) Algorithm and the Brute Force (BF) Search Algorithm, for different N values. Also, it was noticed that, the Proposed GA took more time to provide result than the Proposed SA using GA.
A MULTI-POPULATION BASED FROG-MEMETIC ALGORITHM FOR JOB SHOP SCHEDULING PROBLEMacijjournal
The Job Shop Scheduling Problem (JSSP) is a well known practical planning problem in the
manufacturing sector. We have considered the JSSP with an objective of minimizing makespan. In this
paper, we develop a three-stage hybrid approach called JSFMA to solve the JSSP. In JSFMA,
considering a method similar to Shuffled Frog Leaping algorithm we divide the population in several sub
populations and then solve the problem using a Memetic algorithm. The proposed approach have been
compared with other algorithms for the Job Shop Scheduling and evaluated with satisfactory results on a
set of the JSSP instances derived from classical Job Shop Scheduling benchmarks. We have solved 20
benchmark problems from Lawrence’s datasets and compared the results obtained with the results of the
algorithms established in the literature. The experimental results show that JSFMA could gain the best
known makespan in 17 out of 20 problems.
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
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
this paper, an overview is presented describing various multi objective genetic algorithms developed to
handle different problems with multiple objectives.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
The Internet of Things (IoT) is a revolutionary concept that connects everyday objects and devices to the internet, enabling them to communicate, collect, and exchange data. Imagine a world where your refrigerator notifies you when you’re running low on groceries, or streetlights adjust their brightness based on traffic patterns – that’s the power of IoT. In essence, IoT transforms ordinary objects into smart, interconnected devices, creating a network of endless possibilities.
Here is a blog on the role of electrical and electronics engineers in IOT. Let's dig in!!!!
For more such content visit: https://nttftrg.com/
NUMERICAL SIMULATIONS OF HEAT AND MASS TRANSFER IN CONDENSING HEAT EXCHANGERS...ssuser7dcef0
Power plants release a large amount of water vapor into the
atmosphere through the stack. The flue gas can be a potential
source for obtaining much needed cooling water for a power
plant. If a power plant could recover and reuse a portion of this
moisture, it could reduce its total cooling water intake
requirement. One of the most practical way to recover water
from flue gas is to use a condensing heat exchanger. The power
plant could also recover latent heat due to condensation as well
as sensible heat due to lowering the flue gas exit temperature.
Additionally, harmful acids released from the stack can be
reduced in a condensing heat exchanger by acid condensation. reduced in a condensing heat exchanger by acid condensation.
Condensation of vapors in flue gas is a complicated
phenomenon since heat and mass transfer of water vapor and
various acids simultaneously occur in the presence of noncondensable
gases such as nitrogen and oxygen. Design of a
condenser depends on the knowledge and understanding of the
heat and mass transfer processes. A computer program for
numerical simulations of water (H2O) and sulfuric acid (H2SO4)
condensation in a flue gas condensing heat exchanger was
developed using MATLAB. Governing equations based on
mass and energy balances for the system were derived to
predict variables such as flue gas exit temperature, cooling
water outlet temperature, mole fraction and condensation rates
of water and sulfuric acid vapors. The equations were solved
using an iterative solution technique with calculations of heat
and mass transfer coefficients and physical properties.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
HEAP SORT ILLUSTRATED WITH HEAPIFY, BUILD HEAP FOR DYNAMIC ARRAYS.
Heap sort is a comparison-based sorting technique based on Binary Heap data structure. It is similar to the selection sort where we first find the minimum element and place the minimum element at the beginning. Repeat the same process for the remaining elements.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
Steel & Timber Design according to British Standard
Genetic algorithms mahyar
1. Genetic algorithm with
dynamic population for solving
the simultaneous optimization
of multiple query orders
Mahyar Teymournezhad
m_teimoornezhad@yahoo.com
2. Abstract
The purpose of optimizing several query orders is to find executive
designs that minimize the total cost of executing queries using these
schemes. Each query can have multiple designs individually. Since each
project consists of a series of tasks, the MQO's goal is to find plans
sharing the most tasks with other schemes. In the general case, this is
one of the NP – Complete issues. So far, different methods have been
proposed for this problem. In this paper, the problem of optimizing
multiple query orders is solved using a dynamic population genetic
algorithm. The results show that the proposed method has lower
implementation time and more convergence speed than existing methods.
Key Words : Multi-query optimization, Genetic Algorithm, Link Sequence
3. I. INTRODUCTION
One of the most important and costly parts of the database is the optimization
of query orders.
In Section 3, the modeling of the MQO problem using the genetic algorithm is
being investigated.
If multiple query orders are requested simultaneously from the database,
obtaining an execution plan with minimal cost of executing these orders is a
database optimizer task.
Using the formulation [4], the second phase has been studied independently of
the first phase.
Usually, the second phase of MQO is the most time consuming phase in the
problem.
But to solve multiple query orders simultaneously and identify their shared
tasks, it is necessary for the entire set of execution plans for these queries to
run together, because costly design tasks may lead to more sharing with other
queries and cause getting a better solution to the MQO problem.
4. One of the most famous exploratory techniques used
to optimize complex problems is the genetic algorithm.
A definition of it is given in [8], the genetic algorithm is
used to solve many of the NP – Complete issues.
Goldberg has shown in [4] the practicality of the
genetic algorithm by presenting a summary of
applications of the genetic algorithm. The genetic
algorithm simulates the evolutionary concepts of
biology. This simulation involves probabilistic methods
using evolutionary principles [1]. In the genetic
algorithm, the original data structure is a vector of
genes (called the chromosome). Each chromosome
representing a sample is a solution to the problem. The
chromosome members (called genes) are part of the
problem solution. The quality of the solution sample
(ie, a chromosome) is defined by being close to the
optimal solution (which is called the fitness function).
II. A review of the genetic algorithm
5. The genetic algorithm searches for an optimal solution
using evolutionary operators (also called genetic
operators). Initially, with a randomly weak state,
chromosomes are produced to display a variety of
solutions. Then, genetic operators apply to weak
chromosomes and produce new chromosomes for the
next stage.
II. A review of the genetic algorithm
6. • The three operators used in the genetic algorithm are
as follows:
• Intersection operator: In the intersection operator,
a part of the parent's chromosome changes to make
the child's chromosome.
• Mutation Operator: New chromosomes are
produced by random modification of a small number
of genes in the chromosome. The mutation operator
will never apply to the best solution in the
population.
• Selection Operator: This operator determines
which chromosomes should survive for the next
generation.
II. A review of the genetic algorithm
7. • The simplest way can be to determine that better
chromosomes have a greater chance of surviving in
the next generation. This is what really happens in
evolutionary processes. However, sometimes
applying an intersection or mutation operator on an
inappropriate chromosome may produce the
appropriate chromosome [3].
II. A review of the genetic algorithm
8. The most commonly used selection techniques are:
• Cutting Method: First, all chromosomes are arranged in descending
order (from the best to the worst) based on the number assigned to the
fitness function. The n chromosomes above this list are then
transmitted to the next generation with the same probability.
• Race method: R is randomly selected. Then r chromosome is
selected from the population and the chromosome with the best value
(runtime) is transmitted to the next generation. This process continues
until the proper amount has been reached for the next generation. In
this method, a chromosome may be selected several times.
• Fortune Wheel Method: In this method, the chromosomes are
placed on the parts of the circle according to their fitness. The more
chromosomes inside a part have a better fit, the greater the area. Then
a random number is generated and the chromosomes of the part
corresponding to that random number are transmitted to the next
generation. Steinbrunn [9] has used this method.
II. A review of the genetic algorithm
9. • The genetic algorithm can easily model the MQO
problem. Each chromosome is a solution to this problem.
Each gene in the chromosomes represents a plan for the
corresponding query order.
• Each Ci chromosome is composed of a number of Gj
genes. Each gene is a solution to a query order. In a
generation, the number of chromosomes varies depending
on the number of population associated with that Pk.
• Selection Operator Σ: Takes the population of a
generation and selects some of them to be transferred to
the next generation.
• Mutation Operator (M): It takes a chromosome as input
and creates a new chromosome.
III. Modeling of Genetic Algorithm for MQO
10. To select a number of chromosomes for the next generation, the quality
of the chromosomes (which is determined by the fitness function) is
considered. A simple choice for the fitness function is to reverse the
entire execution time of the query order task.
Intersection and mutation operators can also be easily defined for the
MQO problem. These operators create new and valid solutions. Since the
genes of a chromosome represent a selective plan for the query order for
that gene, replacing a plan with the current plan creates a new and valid
solution. This is done by the mutation operator.
For the intersection operator, different types can be considered: single-
point, multi-point, and segment. In the proposed method, all these
techniques create valid solutions. If two chromosomes represent two
valid solutions for the MQO problem, each intersecting operator on these
two chromosomes will create new and valid solutions. Regardless of the
type of intersection operator and the location of doing it, since all the
pieces that move in this operator represent valid solutions for the
corresponding query instructions, the new solutions will also be valid.
III. Modeling of Genetic Algorithm for MQO
11. This paper presents a method in which the size of the population varies
in each generation. So far, in all the classical methods, the population
selection stage is considered constant. By doing this, the algorithm will
be simpler, but an artificial limitation will be created and will not follow
the natural genetic law in biology. Because the size of the population is
constantly changing. One of the drawbacks of the revelation methods is
that the algorithm stops at the local minimum and is also costly in
computing. On the other hand, the phenomenon of congestion is also one
of the destructive factors in the quality of the genetic algorithm [3].
When enough resources are available and fairly good solutions are
available, the size of the population increases, and the size of the
population decreases when the number of appropriate solutions in a
generation is low. At first glance, the proposed method may seem not to
be very effective, but experiments have shown that using this method
improves the accuracy and speed of the implementation of the genetic
algorithm.
IV. Providing a solution offer:
Genetic algorithm with dynamic population size (DP – GA)
12. Although in some generations the algorithm may increase
the population and thus increase the amount of computing,
and cause the algorithm to slow down, the choice of an
appropriate threshold value for how the population changes
will reduce overall run-time. One of the most important
parameters in this method is how to resize the population. To
do so, first, the problem is solved by the greedy method.
This is due to the speed of the implementation of the greedy
algorithm. The resulting number is used by the greedy
algorithm as a threshold value. In the next stage, considering
the difference of the best answer in each generation with the
answer given by the greedy algorithm, the population of the
next generation is determined.
IV. Providing a solution offer:
Genetic algorithm with dynamic population size (DP – GA)
13. In this paper, the population of the new generation is
calculated as follows:
In this formula is the next-gen population, is the
current generation population, is the time calculated by
the greedy algorithm and is the time of
execution of the best solution in the current generation.
IV. Providing a solution offer:
Genetic algorithm with dynamic population size (DP – GA)
14. In this section, the experimental results of comparing the
genetic algorithm with constant population and proposed
genetic algorithm with dynamic population are presented.
Experiments are performed on computers with 2.2 GHz and
2G main memory. The language of the implementation of
algorithms is C.
V. The experimental results and analysis
15. • To generate input, the parameters of Table 1 are used and
are as follows:
• At first tasks are generated randomly. To produce them, the
two parameters of the number of tasks (T) and the lowest
and the highest amount for the time of execution of tasks are
used. Initially “T” tasks are produced (ie. “T” is the number
of tasks). Then, for each generated task, a number is
allocated in the MinET and MaxET intervals as runtime.
After the tasks are created, they are distributed among the
plans. For this purpose, the parameters MinP and MaxP are
used. Although there may be many plans for using the tasks
in share, it is avoided to create two exactly identical plans.
Finally, query instructions are created. To generate them, the
parameter of the number of query orders (Q), MinQ and
MaxQ are used.
V. The experimental results and analysis
16. • Each query order has its own set of plans for execution.
Therefore, a particular scheme does not solve more than one
query command. At the same time, since each query order
consists of a plan and each plan consists of tasks, these tasks
can be shared between query orders.
TABLE I: The values used to simulate the MQO problem with genetic algorithm
V. The experimental results and analysis
Parameter name Amount
Primary population size 100
Iteration count (number of generations) 10
Mutation rate %1
17. To calculate the input size, the average values (MaxP, MinP)
and (MaxQ, MinQ) are considered, so the input size will be
equal to:
In fact, the input size is equal to the size of the MQO
problem search space. This number does not correlate with
the optimal solution value. Although a query order can have
several executable plans, only one of these plans will appear
for each query prompt in the final answer. Therefore, the
optimal solution value is related only to the number of query
orders, the number of tasks in each plan, and the execution
time of the tasks.
V. The experimental results and analysis
18. To compare the exploratory algorithms that are presented to solve
NP – Complete problems, there are usually two aspects to
consider: The algorithm execution time and the difference in the
solution obtained by the algorithm with the optimal response.
Bayir in [1] explains nine different modes of genetic algorithm
and has shown that for the problem of optimizing several query
orders, the use of the shear selection operator and the initial
population size operator is used only in the first execution of the
algorithm. In future generations, this amount will be less or more
depending on the best answer of every generation. The rate of
mutation states that in each generation of the algorithm, several
percent of the chromosomes of that generation will mutate. In the
following, we use the phrase "time to execute the chromosome" as
the time of execution of tasks that are given as the input of the
MQO problem to the genetic algorithm.
V. The experimental results and analysis
19. The results are shown in Figures 1 and 2. The GA_DP algorithm
shows more precision in finding the optimal answer. The reason
for this is using a variable population that allows the algorithm to
pass through the local minimum and search for more space. It is
also shown in Figure 2 that the speed of the proposed algorithm is
also increased. In cases where the execution time of the
chromosomes of a generation has a significant difference with the
optimal solution, the size of the population decreases. This
decrease in population causes an increase in the speed of the
algorithm and, as noted in Section 4, increases the convergence
rate to the answer in these cases.
V. The experimental results and analysis
20. V. The experimental results and analysis
250 350 450 550 650 750
Figure 1. Response time chart
21. V. The experimental results and analysis
250 350 450 550 650 750
Figure 2. Runtime graph
22. VI. CONCLUSION
In this paper, a solution based on a genetic algorithm with variable population
was presented. One of the main drawbacks of the genetic algorithm is the
timing of computing and getting caught up in local extremums. By the
proposed method, although in some cases the amount of computing in one
generation of the algorithm may be increased, overall, the amount of
computations is reduced and, as a result, the algorithm's speed will be
increased. Also, due to the population growth in the conditions mentioned in
the article, this allows a proposed algorithm to pass through local minimums
and has a faster convergence rate than the base genetic algorithm. The
disadvantage of this method is that we need to set the initial parameters and
the appropriate threshold for the rate of population change. The greedy
method was used as threshold value in this paper. It seems that using random
methods such as random walk and recovery iteration are more accurate
estimations to determine this amount.
23. REFERENCES
[1] Murat Ali Bayir, Ismail H. Toroslu, and Ahmet Cosar, Genetic Algorithm for the Multiple-Query Optimization
Problem, 2007, IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART C: APPLICATIONS
AND REVIEWS, VOL. 37, NO. 1, JANUARY 2007
[2] Guido Moerkotte, Building Query Compilers, Page [375- 385], 2006
[3] Tom M. Mitchell, Machine Learning, page [250-270] 1997
[4] D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning.
Reading, MA: Addison-Wesley, 1989
[5] T. Sellis, “Multiple query optimization,” ACM Trans. Database Syst.,vol. 13, no. 1, pp. 23–
52, 1988.
[6] A. Cosar, E. P. Lim, and J. Srivastava, “Multiple query optimization with depth-first branch-and-bound and
dynamic query ordering,” in Proc.CIKM 93, 1993, pp. 433–438.
[7] K. Shim, T. Sellis, and D. Nau, “Improvements on a heuristic algorithm for multiple-query
optimization,” Data Knowl. Eng., vol. 12, no. 2, pp. 197– 222, 1994.
[8] J. H. Holland, Adaptation in Natural and Artificial Systems. Ann Arbor,MI: Univ. Michigan
Press, 1975.
[9] Michael Steinbrunn, Guido Moerkotte, Alfons Kemper, Heuristic and randomized
optimization for the join ordering problem,The VLDB Journal(1997)6:191–208