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.
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.
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.
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.
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.
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
This presentation discusses the following topics:What is Genetic Algorithms?
Introduction to Genetic Algorithm
Classes of Search Techniques
Components of a GA
Components of a GA
Simple Genetic Algorithm
GA Cycle of Reproduction
Population
Reproduction
Chromosome Modification: Mutation, Crossover, Evaluation, Deletion
Example
GA Technology
Issues for GA Practitioners
Benefits of Genetic Algorithms
GA Application Types
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.
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
This presentation discusses the following topics:What is Genetic Algorithms?
Introduction to Genetic Algorithm
Classes of Search Techniques
Components of a GA
Components of a GA
Simple Genetic Algorithm
GA Cycle of Reproduction
Population
Reproduction
Chromosome Modification: Mutation, Crossover, Evaluation, Deletion
Example
GA Technology
Issues for GA Practitioners
Benefits of Genetic Algorithms
GA Application Types
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 is an easy introduction to the concept of Genetic Algorithms. It gives Simple explanation of Genetic Algorithms. Covers the major steps that are required to implement the GA for your tasks.
For other resources visit: http://pimpalepatil.googlepages.com/
For more information mail me on pbpimpale@gmail.com
Modeling selection pressure in XCS for proportionate and tournament selectionkknsastry
In this paper, we derive models of the selection pressure in XCS for proportionate (roulette wheel) selection and tournament selection. We show that these models can explain the empirical results that have been previously presented in the literature. We validate the models on simple problems showing that, (i) when the model assumptions hold, the theory perfectly matches the empirical evidence; (ii) when the model assumptions do not hold, the theory can still provide qualitative explanations of the experimental results.
The genetic algorithm is a mataheuristic method that uses the metaphor of the evolutionary process of living things, especially Darwin's theory of evolution. This persentation will discuss about the fundamental of Genetic Algorithm. Download this PPT and put in "Slide Persentation (F5)" to play the animation in it.
Solution to the practice test ch 10 correlation reg ch 11 gof ch12 anovaLong Beach City College
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Elementary Statistics Practice Test 5
Module 5
Chapter 10: Correlation and Regression
Chapter 11: Goodness of Fit and Contingency Tables
Chapter 12: Analysis of Variance
Top of Form1. Stream quality is based on the levels of many .docxedwardmarivel
Top of Form
1.
Stream quality is based on the levels of many variables, including the following. Which of these variables is quantitative?
The amount of dissolved oxygen
The number of distinct species present
The amount of phosphorus
All of the above
2.
Which of the following is a discrete variable?
Weight of a fish
Length of a fish
None of the above
Number of toxins present in a fish
3.
During winter, red foxes hunt small rodents by jumping into thick snow cover. Researchers report that a hunting trip lasts on average 19 minutes and involves on average 7 jumps. They also report that, surprisingly, 79% of all successful jumps are made in the northeast direction. Three variables are mentioned in this report. The first variable mentioned is
ordinal.
quantitative and discrete.
quantitative and continuous.
categorical.
4.
A sample of 55 streams in severe distress was obtained during 2007. The following is a bar graph of the number of streams that are from the Northeast, Northwest, Southeast, or Southwest. In the bar graph, the bar for the Northeast has been omitted.
The number of streams from the Northeast is
35.
25.
15.
45.
5.
Here is a stemplot (with split stems) of body temperatures (in degrees Fahrenheit) for 65 healthy adult women.
The first quartile for this data set is
97.6.
97.5.
98.0.
97.9.
6.
Researchers measured the length of the central retrix (R1), a flight-involved tail feather, in 21 female long-tailed finches. Here is a boxplot of the length, in millimeters (mm).
Based on this boxplot, which of the following statements is TRUE?
The distribution of R1 lengths is bimodal.
The distribution of R1 lengths is mildly right-skewed with a high outlier.
75% of the birds in this study had an R1 length above 70 mm.
All of the above
7.
Geckos are lizards with specialized toe pads that enable them to easily climb all sorts of surfaces. A research team examined the adhesive properties of 7 Tokay geckos. Below are their toe-pad areas (in square centimeters, cm2).
5.6
4.9
6.0
5.1
5.5
5.1
7.5
To be an outlier, an observation must fall outside the range
4.9 to 7.5.
4.2 to 6.9.
3.75 to 7.35.
5.1 to 6.0.
8.
The median age of five people on a committee is 30 years. One of the members, whose age is 50 years, resigns. The median age of the remaining four people in the committee is
not able to be determined from the information given.
25 years.
30 years.
40 years.
9.
By inspection, determine which of the following sets of numbers has the smallest standard deviation.
7, 8, 9, 10
0, 0, 10, 10
0, 1, 2, 3
5, 5, 5, 5
10.
The volume of oxygen consumed (in liters per minute) while a person is at rest and while he or she is exercising (running on a treadmill) was measured for each of 50 subjects. The goal is to determine if the volume of oxygen consumed during aerobic exercise can be estimated from the amount consumed at rest. The results are plotted below.
The scatterplot sugges ...
A Moment Inequality for Overall Decreasing Life Class of Life Distributions w...inventionjournals
:A moment inequality is derived for the system whose life distribution is in an overall decreasing life (ODL) class of life distributions. A new nonparametric test statistic for testing exponentiality against ODL is investigated based on this inequality. The asymptotic normality of the proposed statistic is presented. Pitman's asymptotic efficiency, power and critical values of this test are calculated to assess the performance of the test. Real examples are given to elucidate the use of the proposed test statistic in the reliability analysis. Wealso proposed a test for testing exponentiality versus ODL for right censored data and the power estimates of this test are also simulated for censored data for some commonly used distributions in reliability. Finally, real data are used as an example for practical problems.
Computational Pool-Testing with Retesting StrategyWaqas Tariq
Pool testing is a cost effective procedure for identifying defective items in a large population. It also improves the efficiency of the testing procedure when imperfect tests are employed. This study develops computational pool-testing strategy based on a proposed pool testing with re-testing strategy. Statistical moments based on this applied design have been generated. With advent of computers in 1980‘s, pool-testing with re-testing strategy under discussion is handled in the context of computational statistics. From this study, it has been established that re-testing reduces misclassifications significantly as compared to Dorfman procedure although re-testing comes with a cost i.e. increase in the number of tests. Re-testing considered improves the sensitivity and specificity of the testing scheme.
The Impact of Artificial Intelligence on Modern Society.pdfssuser3e63fc
Just a game Assignment 3
1. What has made Louis Vuitton's business model successful in the Japanese luxury market?
2. What are the opportunities and challenges for Louis Vuitton in Japan?
3. What are the specifics of the Japanese fashion luxury market?
4. How did Louis Vuitton enter into the Japanese market originally? What were the other entry strategies it adopted later to strengthen its presence?
5. Will Louis Vuitton have any new challenges arise due to the global financial crisis? How does it overcome the new challenges?Assignment 3
1. What has made Louis Vuitton's business model successful in the Japanese luxury market?
2. What are the opportunities and challenges for Louis Vuitton in Japan?
3. What are the specifics of the Japanese fashion luxury market?
4. How did Louis Vuitton enter into the Japanese market originally? What were the other entry strategies it adopted later to strengthen its presence?
5. Will Louis Vuitton have any new challenges arise due to the global financial crisis? How does it overcome the new challenges?Assignment 3
1. What has made Louis Vuitton's business model successful in the Japanese luxury market?
2. What are the opportunities and challenges for Louis Vuitton in Japan?
3. What are the specifics of the Japanese fashion luxury market?
4. How did Louis Vuitton enter into the Japanese market originally? What were the other entry strategies it adopted later to strengthen its presence?
5. Will Louis Vuitton have any new challenges arise due to the global financial crisis? How does it overcome the new challenges?
NIDM (National Institute Of Digital Marketing) Bangalore Is One Of The Leading & best Digital Marketing Institute In Bangalore, India And We Have Brand Value For The Quality Of Education Which We Provide.
www.nidmindia.com
New Explore Careers and College Majors 2024.pdfDr. Mary Askew
Explore Careers and College Majors is a new online, interactive, self-guided career, major and college planning system.
The career system works on all devices!
For more Information, go to https://bit.ly/3SW5w8W
This comprehensive program covers essential aspects of performance marketing, growth strategies, and tactics, such as search engine optimization (SEO), pay-per-click (PPC) advertising, content marketing, social media marketing, and more
Want to move your career forward? Looking to build your leadership skills while helping others learn, grow, and improve their skills? Seeking someone who can guide you in achieving these goals?
You can accomplish this through a mentoring partnership. Learn more about the PMISSC Mentoring Program, where you’ll discover the incredible benefits of becoming a mentor or mentee. This program is designed to foster professional growth, enhance skills, and build a strong network within the project management community. Whether you're looking to share your expertise or seeking guidance to advance your career, the PMI Mentoring Program offers valuable opportunities for personal and professional development.
Watch this to learn:
* Overview of the PMISSC Mentoring Program: Mission, vision, and objectives.
* Benefits for Volunteer Mentors: Professional development, networking, personal satisfaction, and recognition.
* Advantages for Mentees: Career advancement, skill development, networking, and confidence building.
* Program Structure and Expectations: Mentor-mentee matching process, program phases, and time commitment.
* Success Stories and Testimonials: Inspiring examples from past participants.
* How to Get Involved: Steps to participate and resources available for support throughout the program.
Learn how you can make a difference in the project management community and take the next step in your professional journey.
About Hector Del Castillo
Hector is VP of Professional Development at the PMI Silver Spring Chapter, and CEO of Bold PM. He's a mid-market growth product executive and changemaker. He works with mid-market product-driven software executives to solve their biggest growth problems. He scales product growth, optimizes ops and builds loyal customers. He has reduced customer churn 33%, and boosted sales 47% for clients. He makes a significant impact by building and launching world-changing AI-powered products. If you're looking for an engaging and inspiring speaker to spark creativity and innovation within your organization, set up an appointment to discuss your specific needs and identify a suitable topic to inspire your audience at your next corporate conference, symposium, executive summit, or planning retreat.
About PMI Silver Spring Chapter
We are a branch of the Project Management Institute. We offer a platform for project management professionals in Silver Spring, MD, and the DC/Baltimore metro area. Monthly meetings facilitate networking, knowledge sharing, and professional development. For event details, visit pmissc.org.
2. Outline
Introduction to Genetic Algorithm (GA)
GA Components
Representation
Recombination
Mutation
Parent Selection
Survivor selection
Example
2
3. Slide sources
Most of the contents are taken from :
Genetic Algorithms: A Tutorial By Dr. Nysret Musliu,
Associate Professor Database and Artificial Intelligence
Group, Vienna University of Technology.
Introduction to Genetic Algorithms, Assaf Zaritsky Ben-
Gurion University, Israel (www.cs.bgu.ac.il/~assafza)
3
4. Introduction to GA (1)
4
Calculus Base
Techniques
Fibonacci
Search Techniqes
Guided random search
techniqes
Enumerative
Techniqes
BFSDFS Dynamic
Programming
Tabu Search Hill
Climbing
Simulated
Anealing
Evolutionary
Algorithms
Genetic
Programming
Genetic
Algorithms
Sort
5. Introduction to GA (2)
“Genetic Algorithms are good at taking large, potentially huge
search spaces and navigating them, looking for optimal
combinations of things, solutions you might not otherwise
find in a lifetime.”- Salvatore Mangano, Computer Design,
May 1995.
Originally developed by John Holland (1975)
The genetic algorithm (GA) is a search heuristic that
mimics the process of natural evolution
Uses concepts of “Natural Selection” and “Genetic
Inheritance” (Darwin 1859)
5
6. Use of GA
Widely-used in business, science and
engineering
Optimization and Search Problems
Scheduling and Timetabling
6
7. Let’s Learn Biology (1)
Our body is made up of trillions of cells. Each cell has a
core structure (nucleus) that contains your
chromosomes.
Each chromosome is made up of tightly coiled strands
of deoxyribonucleic acid (DNA). Genes are segments of
DNA that determine specific traits, such as eye or hair
color. You have more than 20,000 genes.
A gene mutation is an alteration in your DNA. It can be
inherited or acquired during your lifetime, as cells age or
are exposed to certain chemicals. Some changes in your
genes result in genetic disorders.
7
10. Let’s Learn Biology (4)
Natural Selection
Darwin's theory of evolution: only the organisms best
adapted to their environment tend to survive and
transmit their genetic characteristics in increasing
numbers to succeeding generations while those less
adapted tend to be eliminated.
10
Source: http://www.bbc.co.uk/programmes/p0022nyy
11. GA is inspired from Nature
A genetic algorithm maintains a population of
candidate solutions for the problem at hand,
and makes it evolve by iteratively applying a set
of stochastic operators
11
12. Nature VS GA
The computer model introduces simplifications
(relative to the real biological mechanisms),
BUT
surprisingly complex and interesting structures
have emerged out of evolutionary algorithms
12
13. High-level Algorithm
produce an initial population of individuals
evaluate the fitness of all individuals
while termination condition not met do
select fitter individuals for reproduction
recombine between individuals
mutate individuals
evaluate the fitness of the modified individuals
generate a new population
End while
13
15. GA Components With Example
The MAXONE problem : Suppose we want to
maximize the number of ones in a string of L
binary digits
It may seem trivial because we know the answer
in advance
However, we can think of it as maximizing the
number of correct answers, each encoded by 1,
to L yes/no difficult questions`
15
17. Algorithm
produce an initial population of individuals
evaluate the fitness of all individuals
while termination condition not met do
select fitter individuals for reproduction
recombine between individuals
mutate individuals
evaluate the fitness of the modified individuals
generate a new population
End while
17
18. Initial Population
We start with a population of n random strings. Suppose that l = 10 and n
= 6
We toss a fair coin 60 times and get the following initial population:
s1 = 1111010101
s2 = 0111000101
s3 = 1110110101
s4 = 0100010011
s5 = 1110111101
s6 = 0100110000
18
19. Algorithm
produce an initial population of individuals
evaluate the fitness of all individuals
while termination condition not met do
select fitter individuals for reproduction
recombine between individuals
mutate individuals
evaluate the fitness of the modified individuals
generate a new population
End while
19
20. Fitness Function: f()
We toss a fair coin 60 times and get the following initial population:
s1 = 1111010101 f (s1) = 7
s2 = 0111000101 f (s2) = 5
s3 = 1110110101 f (s3) = 7
s4 = 0100010011 f (s4) = 4
s5 = 1110111101 f (s5) = 8
s6 = 0100110000 f (s6) = 3
---------------------------------------------------
= 34
20
21. Algorithm
produce an initial population of individuals
evaluate the fitness of all individuals
while termination condition not met do
select fitter individuals for reproduction
recombine between individuals
mutate individuals
evaluate the fitness of the modified individuals
generate a new population
End while
21
22. Selection (1)
Next we apply fitness proportionate selection
with the roulette wheel method:
We repeat the extraction as many times as the
number of individuals
we need to have the same parent population
size (6 in our case)
22
Individual i will have a
probability to be chosen
i
if
if
)(
)(
21
n
3
Area is
Proportional to
fitness value
4
23. Selection (2)
Suppose that, after performing selection, we get
the following population:
s1` = 1111010101 (s1)
s2` = 1110110101 (s3)
s3` = 1110111101 (s5)
s4` = 0111000101 (s2)
s5` = 0100010011 (s4)
s6` = 1110111101 (s5)
23
24. Algorithm
produce an initial population of individuals
evaluate the fitness of all individuals
while termination condition not met do
select fitter individuals for reproduction
recombine between individuals
mutate individuals
evaluate the fitness of the modified individuals
generate a new population
End while
24
25. Recombination (1)
aka Crossover
For each couple we decide according to
crossover probability (for instance 0.6) whether
to actually perform crossover or not
Suppose that we decide to actually perform
crossover only for couples (s1`, s2`) and (s5`,
s6`).
For each couple, we randomly extract a
crossover point, for instance 2 for the first and 5
for the second
25
27. Algorithm
produce an initial population of individuals
evaluate the fitness of all individuals
while termination condition not met do
select fitter individuals for reproduction
recombine between individuals
mutate individuals
evaluate the fitness of the modified individuals
generate a new population
End while
27
29. Mutation (2)
The final step is to apply random mutation: for
each bit that we are to copy to the new
population we allow a small probability of error
(for instance 0.1)
Causes movement in the search space
(local or global)
Restores lost information to the population
29
30. High-level Algorithm
produce an initial population of individuals
evaluate the fitness of all individuals
while termination condition not met do
select fitter individuals for reproduction
recombine between individuals
mutate individuals
evaluate the fitness of the modified individuals
generate a new population
End while
30
31. Fitness of New Population
After Applying Mutation:
s1``` = 1110100101 f (s1```) = 6
s2``` = 1111110100 f (s2```) = 7
s3``` = 1110101111 f (s3```) = 8
s4``` = 0111000101 f (s4```) = 5
s5``` = 0100011101 f (s5```) = 5
s6``` = 1110110001 f (s6```) = 6
-------------------------------------------------------------
37
31
32. Example (End)
In one generation, the total population fitness
changed from 34 to 37, thus improved by ~9%
At this point, we go through the same process
all over again, until a stopping criterion is met
32
34. Issues
Choosing basic implementation issues:
representation
population size, mutation rate, ...
selection, deletion policies
crossover, mutation operators
Termination Criteria
Performance, scalability
Solution is only as good as the evaluation function
(often hardest part)
34
35. When to Use a GA
Alternate solutions are too slow or overly complicated
Need an exploratory tool to examine new approaches
Problem is similar to one that has already been
successfully solved by using a GA
Want to hybridize with an existing solution
Benefits of the GA technology meet key problem
requirements
37. References
Genetic Algorithms: A Tutorial By Dr. Nysret Musliu , Associate Professor
Database and Artificial Intelligence Group, Vienna University of Technology.
Introduction to Genetic Algorithms, Assaf Zaritsky Ben-Gurion University,
Israel (www.cs.bgu.ac.il/~assafza)
37