Data Science - Part XIV - Genetic AlgorithmsDerek Kane
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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 Algorithms(GAs) are adaptive heuristic search algorithms that belong to the larger part of evolutionary algorithms. Genetic algorithms are based on the ideas of natural selection and genetics. These are intelligent exploitation of random search provided with historical data to direct the search into the region of better performance in solution space. They are commonly used to generate high-quality solutions for optimization problems and search problems.
This presentations explains a search heuristic proved to be very efficient for "unknown" reasons, named Genetic Algorithm.
CONTENTS
What is it?
Analogy to Biological Evolution
Key Concepts (Initial Population, Fitness Functions, Selection, Crossover, Mutation)
Methodology
Issues
When to Use GA?
Summary
Data Science - Part XIV - Genetic AlgorithmsDerek Kane
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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 Algorithms(GAs) are adaptive heuristic search algorithms that belong to the larger part of evolutionary algorithms. Genetic algorithms are based on the ideas of natural selection and genetics. These are intelligent exploitation of random search provided with historical data to direct the search into the region of better performance in solution space. They are commonly used to generate high-quality solutions for optimization problems and search problems.
This presentations explains a search heuristic proved to be very efficient for "unknown" reasons, named Genetic Algorithm.
CONTENTS
What is it?
Analogy to Biological Evolution
Key Concepts (Initial Population, Fitness Functions, Selection, Crossover, Mutation)
Methodology
Issues
When to Use GA?
Summary
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
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Francesca Gottschalk from the OECDâs Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
Palestine last event orientationfvgnh .pptxRaedMohamed3
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An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
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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.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
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Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
⢠The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
⢠The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate âany matterâ at âany timeâ under House Rule X.
⢠The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
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
The Roman Empire A Historical Colossus.pdfkaushalkr1407
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The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesarâs dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empireâs birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empireâs society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
Operation âBlue Starâ is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
2. Genetic Algorithms
â˘Genetic Algorithms are part of evolutionary computing.
â˘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.
⢠Optimization is the process of making something better. In any process, we have a set of inputs
and a set of outputs
â˘Optimization refers to finding the values of inputs in such a way that we get the âbestâ output
values.
â˘The definition of âbestâ varies from problem to problem, but in mathematical terms, it refers to
maximizing or minimizing one or more objective functions, by varying the input parameters.
3. Search Space
ď§If We are solving some problem, we are usually looking for solutions best among others.
ď§The space of all feasible solutions is called Search Space( State space).
ď§Each point in the Search space represent one feasible solution. Each feasible solution can be
marked by its value of fitness for the problem.
ď§Various techniques to find suitable solution are Hill Climbing, Best-First Search etc.
ď§The solution is often considered as good solution because it is not possible to prove what is real
optimum.
ď§NP-Hard Problems are kind of difficult problems which cannot be solved by traditional way.
ď§It is difficult to find solution but once we have it, it is easy to check the solution. This fact led to
NP-Complete Problem.
4. Search Space(contd.)
ď§NP- means Non-Deterministic Polynomial
ď§ It means that it is possible to guess the solution( by some non-deterministic algorithm) and
then check it, both in polynomial time.
ď§ NP-Complete problems are of type for which answer is simply âYesâ or âNoâ.
ď§ The problems with complicated outputs are called NP-Hard problems.
ď§ To find a solution, simply try all the alternatives but this is very slow and sometimes not
usable at all.
ď§ Genetic Algorithms are alternative method to solve NP-Problem. E.g. Travelling Salesman
Problem
5. Principle behind Genetic Algorithms
â˘SELECT THE BEST, DISCARD THE REST
â˘It is based on Darwinâs principle of natural selection.
⢠If there are Organisms that reproduce, and
⢠If Offsprings inherit traits from there progenitors, and
⢠If there is variability of traits, and
⢠If the environment cannot support all members of a growing population,
⢠Then those members of the population with less adaptive traits( determined by the
environment) will die out, and
⢠Those members with more adaptive traits( determined by the environment ) will thrive.
⢠The Result is evolution of Species.
6. Evolution through Natural Selection
Initial Population of Animals
Struggle for existence, survival of the fittest
Surviving individuals reproduce, propagate favourable characteristics
Millions of Years
Evolved Species
[Favourable characteristic now a trait of Species.]
7. Nature to Computer Mapping
NATURE
ďPopulation
ďIndividual
ďFitness
ďChromosome
ďGene
ďReproduction
COMPUTER
ďSet Of Solutions
ďSolution to a Problem
ďQuality of a Solution
ďEncoding for a Solution
ďPart of encoding
ďCrossover
8. Requirements to implement Genetic
Algorithm
Two important elements required for any problem before a genetic algorithm can be used for a solution are
â˘Method for representing a solution ex: a string of bits, numbers, character ex: determination total weight.
â˘Method for measuring the quality of any proposed solution, using fitness function.
Basi
9. GA
ď§Genetic Algorithm is started with a set of solutions(represented by Chromosomes) called
population.
ď§Solutions from one Population are taken and used to form a new population.( This is motivated
by hope that the new population will be better than the old one)
ď§Solutions which are selected to form new solutions(Offsprings) are selected according to their
fitness value( the more suitable they are, the more chances they have to reproduce.)
ď§This process is repeated until some condition is satisfied. E.g. No of Populations or improvement
of the best solution.
10. Genetic Algorithm
Step 1- [START] Generate random population of n Chromosomes i.e. suitable solutions to the
problem.
Step 2- [Fitness] Evaluate the fitness f(x) of each Chromosome x in the Population.
Step 3-[New Population] Create a new population by repeating following steps until the new
population is complete.
⌠(a) [Selection] Select two parent Chromosomes from a Population according to their
fitness.(the better fitness, the bigger chances to be selected)
⌠(b)[Crossover] With a crossover probability, cross over the parents to form a new
offspring(children). If no crossover was performed, offspring is an exact copy of parents.
⌠( c)[Mutation] With a mutation probability, mutate new offspring at each locus( position in
chromosome)
11. Genetic Algorithm(contd.)
⌠( d)[Accepting] Place new offspring in a new Population.
Step 4- [Replace] Use new generated population for a further run of algorithm.
Step 5- [Test] If the end condition is satisfied, Stop, and return the best Solution in current
Population.
Step 6- [Loop] Go To Step 2.