Presentation is about genetic algorithms. Also it includes introduction to soft computing and hard computing. Hope it serves the purpose and be useful for reference.
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.
Presentation is about genetic algorithms. Also it includes introduction to soft computing and hard computing. Hope it serves the purpose and be useful for reference.
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.
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.
This presentation is intended for giving an introduction to Genetic Algorithm. Using an example, it explains the different concepts used in Genetic Algorithm. If you are new to GA or want to refresh concepts , then it is a good resource for you.
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.
Evolutionary algorithms are stochastic search and optimization heuristics derived from the classic evolution theory, which are implemented on computers in the majority of cases.
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.
This presentation is intended for giving an introduction to Genetic Algorithm. Using an example, it explains the different concepts used in Genetic Algorithm. If you are new to GA or want to refresh concepts , then it is a good resource for you.
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.
Evolutionary algorithms are stochastic search and optimization heuristics derived from the classic evolution theory, which are implemented on computers in the majority of cases.
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.
Traveling Salesman Problem (TSP) is a kind of NPHard problem which cant be solved in polynomial time for
asymptotically large values of n. In this paper a balanced combination of Genetic algorithm and Simulated Annealing is used. To
improve the performance of finding optimal solution from huge
search space, we have incorporated the use of tournament and
rank as selection operator. And Inver-over operator Mechanism
for crossover and mutation . To illustrate it more clearly an
implementation in C++ (4.9.9.2) has been done.
Index Terms—Genetic Algorithm (GA) , Simulated Annealing
(SA) , Inver-over operator , Lin-Kernighan algorithm , selection
operator , crossover operator , mutation operator.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Introduction to Optimization with Genetic Algorithm (GA)Ahmed Gad
Selection of the optimal parameters for machine learning tasks is challenging. Some results may be bad not because the data is noisy or the used learning algorithm is weak, but due to the bad selection of the parameters values. This article gives a brief introduction about evolutionary algorithms (EAs) and describes genetic algorithm (GA) which is one of the simplest random-based EAs.
References:
Eiben, Agoston E., and James E. Smith. Introduction to evolutionary computing. Vol. 53. Heidelberg: springer, 2003.
https://www.linkedin.com/pulse/introduction-optimization-genetic-algorithm-ahmed-gad
https://www.kdnuggets.com/2018/03/introduction-optimization-with-genetic-algorithm.html
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
3. We will see
◂ Introduction
◂ Terminology
◂ Flow chart
◂ Advantages, disadvantages
and applications.
◂ Conclude with an example
@Harsh_Sinha
4. GENETIC ALGORITHM
◂ Optimization Algorithm
◂ Nature inspired approach based on
Darwin’s law of “survival of the fittest” and
bio-inspired operators such as Pairing,
crossover and mutation
◂ Frequently used to find optimal or near-
optimal solutions of difficult problems
4@Harsh_Sinha
5. GENETIC ALGORITHM
◂ It is not an smart algorithm neither an
intelligence algorithm
Rather it reflects the changes and
response to it very quickly, so it called
Genetic algorithm.
5@Harsh_Sinha
6. OPTIMIZATION
◂ Optimization is the process of making
something better
◂ Finding the values of inputs in such a way
that we get the “best” output value.
6@Harsh_Sinha
8. BRIEF OF TERMINOLOGY
◂ Genes : A gene represents some
data.
◂ Chromosomes : A chromosome
is an array of genes. In some way
it contains information about
solution which it represents thus
it requires encoding
◂ Population: Collection of
chromosomes strings/array.
8
1 0 1 1 0 1
0 0 1 1 1 0
1 1 0 1 0 0
1 0 0 0 0 1
1 1 0 1 1 1
1 0 1 0 1 1
@Harsh_Sinha
11. SELECTION
Selection individual for creating the next
generation(better generation).
In terms of CS selecting the data so to reach optimal
solution.
Selection is done by applying fitness function.
11@Harsh_Sinha
12. FITNESS & FITNESS FUNCTION
Fitness: The value assigned to an individual based on how far or
close an individual is from solution; greater the fitness value
better the solution it contains.
Fitness Function: A function that assigns fitness value to the
individual.It is problem specific.
12@Harsh_Sinha
14. HOW ARE PARENTS SELECTED?
1. Roulette wheel Selection
14
chromosomes Fitness
value
A 9.8
B 7.9
C 2.4
D 4.5
SPIN THE
WHEEL
@Harsh_Sinha
15. HOW ARE PARENTS SELECTED?
2. Rank Selection
Remove the concept of fitness
value while selecting a parent.
Every individual in the
population is ranked according
to their fitness.
15
chromosomes Fitness
value
Rank
A 9.8 1
B 7.9 2
C 2.4 4
D 4.5 3
@Harsh_Sinha
16. HOW ARE PARENTS SELECTED?
3. STOCHASTIC UNIVERSAL SAMPLING(SUS): Multiple fixed points,
all the parents are chosen in just one spin of the wheel.
4.TOURNAMENT SELECTION: Select k individuals from the
population at random and select the best out of these to become a
parent, same process is repeated for selecting the next parent.
16@Harsh_Sinha
18. CROSSOVER
Crossover is a genetic operator that combines(mates) two
chromosomes(parents) to produce a new chromosome(offspring).
The crossover operators are of many types:
1. One simple ways is one-point crossover.
2. The others are two-point, uniform, arithmetic and heuristic
crossovers.
They are selected based on the way chromosomes are encoded.
(Encoded because it is computer science and not biology.)
18@Harsh_Sinha
19. CROSSOVER
ONE-POINT CROSSOVER: A random crossover point is
selected and the tail of its two parents are swapped to get new
offsprings.
19
1 0 1 1 1 0 1 1
1 1 1 0 1 0 0 1
1 0 1 0 1 0 0 1
1 1 1 1 1 0 1 1
Parent Chromosomes Offspring Chromosomes
@Harsh_Sinha
21. MUTATION
◂ Small random tweak in the chromosome, to get a new solution.
Types
1. Bit-flip mutation: Select one or more random bits and flip them.
1. Swap mutation: Select two random bits and swap them.
21
0 1 1 0 1 0 0 1 0 1 0 0 1 1 0 1
1 0 0 1 1 1 0 1 1 1 0 1 1 1 0 0
@Harsh_Sinha
22. 22
Determines which
individual are to be
kicked out and
which are to be
kept in the next
generation.
SURVIVOR SELECTION
@Harsh_Sinha
END
23. When are Genetic Algorithms Useful?
There are at least three situations where genetic algorithms are useful:
1. The objective function is not smooth (i.e., not differentiable).
2. There are multiple local optima.
3. Useful when the search space is very large and there are a large
number of parameters (the meaning of “large” keeps changing).
4. Provides a list of “good” solutions and not just a single solution
For details about each point refer to: https://www.burns-stat.com/documents/tutorials/an-introduction-to-genetic-
algorithms/
23@Harsh_Sinha
24. ITS APPLICATION
1. Optimized Telecommunications Routing
2. Trip, Traffic and Shipment Routing
3. Encryption and Code Breaking
4. Evolvable Hardware
5. Joke and Pun Generation
These are few of them.
For more we can refer to : https://www.brainz.org/15-real-world-applications-genetic-algorithms/
24@Harsh_Sinha
25. Drawbacks
1. Computationally expensive as fitness value is calculated repeatedly
2. Not suited for all problems, especially problems which are
simple and for which derivative information are available
3. GA may not converge to the optimal solutions,if not implemented
properly.
25@Harsh_Sinha