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.
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
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.
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
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.
The GENETIC ALGORITHM is a model of machine learning which derives its behavior from a metaphor of the processes of EVOLUTION in nature. Genetic Algorithm (GA) is a search heuristic that mimics the process of natural selection. This heuristic (also sometimes called a metaheuristic) is routinely used to generate useful solutions to optimization and search problems.
The GENETIC ALGORITHM is a model of machine learning which derives its behavior from a metaphor of the processes of EVOLUTION in nature. Genetic Algorithm (GA) is a search heuristic that mimics the process of natural selection. This heuristic (also sometimes called a metaheuristic) is routinely used to generate useful solutions to optimization and search problems.
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.
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.
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.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Accelerate your Kubernetes clusters with Varnish Caching
Analysis of Parameter using Fuzzy Genetic Algorithm in E-learning System
1. Analysis of Parameter usingFuzzyGenetic
Algorithmin E-learningSystem
Name of Student - Harshal Jain (10103538)
Name of Supervisor - Ms. Mukta Goel
2. A Genetic Algorithm (or GA) is a search technique used in computing to find true
or approximate solutions to optimization and search problems. Genetic
algorithms are categorized as global search heuristics. GA are a particular class
of evolutionary algorithms that use techniques inspired by evolutionary biology
such as inheritance, mutation, selection, and crossover (also called
recombination).
The most common type of genetic algorithm works like this:
A population is created with a group of individuals created randomly.
The individuals in the population are then evaluated.
The evaluation function is provided by the programmer and gives the
individuals a score based on how well they perform at the given task.
Two individuals are then selected based on their fitness, the higher the
fitness, higher the chance of being selected.
These individuals then "reproduce" to create one or more offspring, after
which the offspring are mutated randomly.
This continues until a suitable solution has been found or a certain number
of generations have passed, depending on the needs of the programmer.
3.
4. 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).
Occasionally, the solutions may be "seeded" in areas where optimal solutions are likely to
be found.
5. 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. Other methods rate only a random
sample of the population, as this process may be very time-consuming.
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.
In roulette wheel selection, individuals are given a probability of being
selected that is directly proportionate to their fitness.
Two individuals are then chosen randomly based on these probabilities
and produce offspring.
6. Crossover
The most common type is single point crossover. In single point crossover, you choose a locus
at which you swap the remaining alleles from on parent to the other. This is complex and is
best understood visually. As you can see, the children take one section of the chromosome
from each parent. The point at which the chromosome is broken depends on the randomly
selected crossover point.
This particular method is called single point crossover because only one crossover point
exists. Sometimes only child 1 or child 2 is created, but oftentimes both offspring are created
and put into the new population. Crossover does not always occur, however. Sometimes,
based on a set probability, no crossover occurs and the parents are copied directly to the
new population. The probability of crossover occurring is usually 60% to 70%.
7. Mutation
After selection and crossover, you now have a new population full of individuals. Some
are directly copied, and others are produced by crossover. In order to ensure that the
individuals are not all exactly the same, you allow for a small chance of mutation.
You loop through all the alleles of all the individuals, and if that allele is selected for
mutation, you can either change it by a small amount or replace it with a new value. The
probability of mutation is usually between 1 and 2 tenths of a percent.
Mutation is fairly simple. You just change the selected alleles based on what you feel is
necessary and move on. Mutation is, however, vital to ensuring genetic diversity within
the population.
8. 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
Any Combinations of the above
9. ProblemStatement
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.
10. Solution Approach
The solution approach is to find an optimized approach of
knowledge level of user by finding the appropriate weight
to power up the input value. This will tell us that whether
the knowledge level is very low, low, medium, or high.
11. Support for Novelty
Here, the problem signifies that we have a candidate
solution which is a member of a set of possible solutions to
a given problem (a candidate solution does not have to be
a likely or reasonable solution to the problem—it is simply
in the set that satisfies all constraints; that is, it is in the set
of feasible solutions.). So, in a genetic algorithm,
a population of candidate solutions (called individuals,
creatures, or phenotypes) to an optimization problem is
evolved toward better solutions. Each candidate solution
has a set of properties (its chromosomes or genotype)
which can be mutated and altered; traditionally, solutions
are represented in binary as strings of 0s and 1s, but other
encodings are also possible.
12. Product Perspective
System interfaces: The genetic algorithms ensures efficient output and subsequent visual access for ease-of-use.
User interfaces: The integrated graphs formed the output values shows the knowledge level and make it easy to
decide for every user. The designed genetic algorithm allows the user to see the parameter to use for finding
the knowledge level it, then proceeds to yield the output on the screen.
Hardware interfaces: The system does not have any hardware requirements.
Software interfaces: As the system is coded upon eclipse SDK, the system must require eclipse.
Title: Eclipse Classic 4.2.2 (32-bit)
Author: The Eclipse Foundation (www.eclipse.org)
Communications interfaces: The system does not require any network tools or interfaces.
Memory: A minimum of 180 MB is required by the eclipse SDK. The device must also have adequate memory
for training data, test data and code files.
Operations: The user needs to have the degrees of the user as an input to find the parameter and user will be
given the final result thereafter for the further decisions.
Site adaptation requirements: The file must be compatible and size must not be greater than 30 MB.
Overall Description:
13. Product Functions
Take the degrees of the users
Randomly generate a variable form 0.1 to 0.9 which will used as to
generate the power the degrees. There are 5 inputs in each row now the
generated value is ‘w’ and input is ‘p’ now it finds p^w, where for each
and every p there is distinct value of w.
Then the selection process will select two chromosomes which will used
for crossover and generates the 2 children of those two parents. The
cross over will occur for 80% of population.
After that in mutation we will randomly change the value of any ‘w’
which will lead us to termination.
User Characteristics
Any entity can be a user if they have the degree or anything for which
they have to find out the appropriate solution.
14. Constraints
Reliability requirements: The algorithm designed is reliable for the user though it
sometimes may deviate only a little from the original but then also it can be
considered for the desired output.
Assumption and Dependencies
Basic computer skills are assumed on the part of user.
The user must have basic knowledge of Mysql.
There must be a fully appropriate input, the inputs must not be hypothetical or
assumed only by a small survey.
Apportioning of Requirements
Only one selection method is yet to be considered totally appropriate which may
help finding the solution to this types of problem.
15. Functional Requirements:
There is no authentication to any user because it is a simple code
which requires a candidate solution to perform its feature.
In this project there is a huge use of random values. There are 7 to 8
random values generated in the code according to their use.
User Interface is simply the output of the project which shows the
user an optimized way to its solution.
Data to be used as input cannot be manipulated because a small
difference in any value which is the part of the input will change the
whole solution of the project.
16. Non-Functional Requirements:
Reliability - The system shall be able to provide a level of
precision.
System Requirements – The system requires Eclipse software,
php designer, wampp server.
Database - The database must be regularly updated.
Availability – In case of a crash, the system must be restarted.
Portability – Java, the language the algorithm employs, is
platform independent and php is platform dependent.
There is a need of external interface for the algorithm.
17. Implementation
Take the degrees of the users in the database.
Randomly generate a variable form 0.1 to 0.9 which will used as to
generate the power the degrees. There are 5 inputs in each row now the
generated value is ‘w’ and input is ‘p’ now it finds p^w, where for each
and every p there is distinct value of w.
It forms chromosomes of the population.
Then we use the fitness function to find the fitness values of the
chromosomes.
Then the selection process will select two chromosomes which will be
used for crossover and generates the 2 children of those two parents.
The cross over will occur for 80% of population.
After that in mutation we will randomly change the value of any ‘w’
which will lead us to termination.
18. Findings
The following are the key findings during the course of this
project:
The main finding was that the selection process has been used
two times – 1) to choose randomly any two chromosomes from
population and 2) to choose the chromosomes to be used in the
population after crossover by fitness function.
It is sometimes difficult to find the right parameter to get our
output, it may take many generations to find that which would
take time.
19. Conclusion
Here we conclude that the optimized solution of the candidate
solution may come in the starting generations only or it will take
many generations to come to that solution which is time
consuming. But we will definitely find more accurate solution
which will help us to achieve our aim and using this Genetic
Algorithm for it.
20. Future Work
In the future work this project will be very useful to find the
predicted values according to the field in which this project can be
helpful.
Genetic Algorithm implementation cannot be easily found in java
language for future which may become helpful to anyone needed in
specific language.
Genetic algorithms can be applied to a wide range of problems
which are NP (Nondeterministic ally Polynomial) complete.
GA future work is most useful in travelling salesperson problem,
where a salesperson must find the shortest route through a number
of cities starting and ending at a base city. However more practical
applications include strategy planning, scheduling / time tabling and
machine learning.
21. References
Published Papers:
[1] “Marketing a web-site using a fuzzy logic approach” by IOAN
CONSTANTIN ENACHE
[2] “Ten years of genetic fuzzy systems: current framework and new
trends” by O. Cordon, F. Gomide, F. Herrera, F. Hoffmann, L. Magdalena
[3] Tuning Of Fuzzy Systems Using Genetic Algorithms by Ulrich
Bodenhofer
[4] Solving an aggregate production planning problem by using multi-
objective genetic algorithm - (MOGA) approach by Md. A. Akhtar Hasin