This document discusses genetic algorithms and evolutionary algorithms. It defines genetic algorithms as algorithms that manage populations of coded solutions to search for good solutions. It operates on populations across generations using selection, crossover, and mutation. Key terms discussed include fitness functions, individuals, populations and generations, diversity, and parents and children. The document also introduces differential evolution as a stochastic function optimizer based on populations that uses difference vectors.
Genetic algorithms are based on the evolutionary theory. the main principle is Survival of the fittest, Understanding a GA means understanding the simple, iterative processes that underpin evolutionary change
Genetic algorithms are based on the evolutionary theory. the main principle is Survival of the fittest, Understanding a GA means understanding the simple, iterative processes that underpin evolutionary change
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.
In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on bio-inspired operators such as mutation, crossover and selection.
Data Science - Part XIV - Genetic AlgorithmsDerek Kane
This lecture provides an overview on biological evolution and genetic algorithms in a machine learning context. We will start off by going through a broad overview of the biological evolutionary process and then explore how genetic algorithms can be developed that mimic these processes. We will dive into the types of problems that can be solved with genetic algorithms and then we will conclude with a series of practical examples in R which highlights the techniques: The Knapsack Problem, Feature Selection and OLS regression, and constrained optimizations.
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.
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.
In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on bio-inspired operators such as mutation, crossover and selection.
Data Science - Part XIV - Genetic AlgorithmsDerek Kane
This lecture provides an overview on biological evolution and genetic algorithms in a machine learning context. We will start off by going through a broad overview of the biological evolutionary process and then explore how genetic algorithms can be developed that mimic these processes. We will dive into the types of problems that can be solved with genetic algorithms and then we will conclude with a series of practical examples in R which highlights the techniques: The Knapsack Problem, Feature Selection and OLS regression, and constrained optimizations.
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.
Automobile Management System Project Report.pdfKamal Acharya
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely.
When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.
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.
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.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Event Management System Vb Net Project Report.pdfKamal Acharya
In present era, the scopes of information technology growing with a very fast .We do not see any are untouched from this industry. The scope of information technology has become wider includes: Business and industry. Household Business, Communication, Education, Entertainment, Science, Medicine, Engineering, Distance Learning, Weather Forecasting. Carrier Searching and so on.
My project named “Event Management System” is software that store and maintained all events coordinated in college. It also helpful to print related reports. My project will help to record the events coordinated by faculties with their Name, Event subject, date & details in an efficient & effective ways.
In my system we have to make a system by which a user can record all events coordinated by a particular faculty. In our proposed system some more featured are added which differs it from the existing system such as security.
Vaccine management system project report documentation..pdfKamal Acharya
The Division of Vaccine and Immunization is facing increasing difficulty monitoring vaccines and other commodities distribution once they have been distributed from the national stores. With the introduction of new vaccines, more challenges have been anticipated with this additions posing serious threat to the already over strained vaccine supply chain system in Kenya.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
TECHNICAL TRAINING MANUAL GENERAL FAMILIARIZATION COURSEDuvanRamosGarzon1
AIRCRAFT GENERAL
The Single Aisle is the most advanced family aircraft in service today, with fly-by-wire flight controls.
The A318, A319, A320 and A321 are twin-engine subsonic medium range aircraft.
The family offers a choice of engines
2. Genetic algorithms
• John Holland, (1975). "Adaptation in natural and
artificial systems.”
• Algorithms that manage populations consisting of
coded solutions of problems.
• The search for good solutions is made in the space of
codified solutions.
• Manipulation of populations: selection, crossing and
mutation.
3. Features
• They do not work with the objects, but with a coding of
them.
• The AG carry out a search through a whole generation of
objects, they do not look for a single element.
• They use a health function that gives us information on
how adapted they are.
• The transition rules are non-deterministic probabilistic.
7. • Key idea: Give
preference to the
best individuals,
allowing them to
pass their genes to
the next generation.
Selection operator
The goodness of an individual is calculated with the
fitness function.
10. Crossover operator
• Two individuals of the population
are chosen through the selection
operator.
• A crossing place is randomly chosen.
• The values of the two chains are
exchanged at this point.
• By recombining portions of good
individuals, even better individuals
are created.
12. Operation of the crossing by a point
• Once the parents are
selected, with a Pc
probability, a crossing point
in the parents' chains is
chosen and the two children
are obtained
13. Mutation
• Mutation operator:
• With a certain low
probability, a certain
portion of the new
individuals can mutate their
bits.
• Its purpose is to maintain
diversity within the
population and prevent
premature convergence.
• Mutation and selection (no
crossover) create a
maximum slope and noise
tolerant optimization
algorithm.
14. Dominance
•In nature, most of the species associate a genotype with
a pair of chromosomes, where certain alleles dominate
over others (recessive), so that the phenotype is
determined by the combination of these two
chromosomes and by predominance of alleles .
15. Domination map
• Hollstein developed a system of trialélico
domination including a third allele to have a
dominant 1 and a recessive 1.
16. Classical Algorithms Genetic algorithms
They generate a single
point in each iteration.
The sequence of points
approximates the optimal
solution.
GThere will be a
population of points in
each iteration. The best
point of the population
approximates the optimal
solution.
Select the next point in
the sequence for a
deterministic computation.
Select the next population
by means of a computer
that uses a random
number generator.
17. ¿Por qué funcionan los Algoritmos Genéticos?
• Are the AG bits exchanged only?
• What is behind them?
• Holland created a theorem, called
• "Holland's schemes theorem".
• There are some other theorems, some based
in the analysis of Markov chains:
• Is there a chain of different solutions that allows
reaching the optimal solution?
19. Operations of the GA and Schemes
• Two definitions:
• Schema order: (1,1,0, *, *, *, 1, *, *) => order 4
• Length of the scheme: (1,1,0, *, *, *, 1, *, *) => length 6
• The order of a scheme is the number of fixed positions
• (the number of zeros and ones).
• The length of the scheme is the distance between the first and
the last specific position of the chain.
20. Operations of the GA and Schemes
• Selection: good survival for schemes that represent
good individuals.
• Crossing: good survival for short length schemes.
• Mutation: good survival for low order schemes.
21. Conclusion of the scheme theorem
•Short schemes, low order get
better average.
• The schemes receive an
exponentially increasing number
of individuals
22. Computational aspects
• A large number of health assessments can be
computationally expensive.
• They are completely parallel by nature.
• There are several good schemes for parallel
computing.
24. Genetic Algorithms with continuous
parameters
• One of the problems with binary coding in genetic
algorithms is that you do not normally take advantage
of all the precision of the computer.
• What can be done if you want to use all the possible
precision?
• The answer is to represent the parameters in floating
point.
• When the variable is continuous, this is the most
natural way to represent the numbers. It also has the
advantage that a smaller memory size is required than
for binary storage.
25. Genetic Algorithms with continuous
parameters
• Operators do not usually work at the bit level as in the
binary case, but work at the level of the whole floating-
point number:
• Selection: The chromosomes are ordered according to their
health and we are left with the best members of the
population.
• Crossing: In the simplest methods, one or more points are
chosen on the chromosome to mark the crossing points.
Then the parameters between these points are simply
exchanged between the two parents.
26. Genetic Algorithms with continuous
parameters
• Mutation: With a certain probability, which is usually
between 1% and 20%, the chromosomes that are going to
be mutated are selected.
• Next, the parameters of the chromosome that are to be
mutated are randomly selected.
• Finally, each parameter to be mutated is replaced by
another new random parameter or another new random
parameter is added.
27. Some Genetic Algorithm
Terminology
• Fitness Functions
• The fitness function is the function you want to
optimize. For standard optimization algorithms, this is
known as the objective function.
• The toolbox tries to find the minimum of the fitness
function. You can write the fitness function as an M-file
and pass it as a function handle input argument to the
main genetic algorithm function.
28. Some Genetic Algorithm
Terminology
• Individuals
• An individual is any point to which you can apply the
fitness function. The value of the fitness function for an
individual is its score.
• For example, if the fitness function is the vector (2, 3, 1),
whose length is the number of variables in the problem,
is an individual. The score of the individual (2, 3, 1) is f(2,
-3, 1) = 51. An individual is sometimes referred to as a
genome and the vector entries of an individual as genes.
29. Some Genetic Algorithm
Terminology
• Populations and Generations
• A population is an array of individuals. For example, if the size of
the population is 100 and the number of variables in the fitness
function is 3, you represent the population by a 100-by-3 matrix.
• The same individual can appear more than once in the
population. For example, the individual (2, 3, 1) can appear in
more than one row of the array.
• At each iteration, the genetic algorithm performs a series of
computations on the current population to produce a new
population. Each successive population is called a new generation.
30. Some Genetic Algorithm
Terminology
• Diversity
• Diversity refers to the average distance between individuals in a
population. A population has high diversity if the average distance
is large; otherwise it has low diversity. In the figure, the population
on the left has high diversity, while the population on the right has
low diversity.
• Diversity is essential to the genetic algorithm because it enables
the algorithm to search a larger region of the space.
31. Some Genetic Algorithm
Terminology
• Fitness Values and Best Fitness Values
• The fitness value of an individual is the value of the
fitness function for that individual.
• Because the toolbox finds the minimum of the
fitness function, the best fitness value for a
population is the smallest fitness value for any
individual in the population.
32. Some Genetic Algorithm
Terminology
• Parents and Children
• To create the next generation, the genetic
algorithm selects certain individuals in the current
population, called parents, and uses them to create
individuals in the next generation, called children.
• Typically, the algorithm is more likely to select
parents that have better fitness values.
33. Differential Evolution
• Differential Evolution (DE) is a stochastic function optimizer, based on
populations, that uses the difference vector to disturb the population.
• DE shows advantages of speed and performance over conventional
genetic algorithms.
• DE was originally proposed by Kenneth Price and Rainer Storn [1997].
• The crucial idea behind DE is the scheme for generating vectors of test
parameters in which the difference (with weight) between vectors is
added to a selected vector.