Optimization algorithms and methods
In computer software development, genetic Improvement is the use of optimisation and machine learning techniques, particularly search-based software engineering techniques such as genetic programming to improve existing software.The improved program need not behave identically to the original. For example, automatic bug fixing improves program code by reducing or eliminating buggy behaviour.In other cases the improved software should behave identically to the old version but is better because,for example:it runs faster,it uses less memory,it uses less energyorit runs on a different type of computer.GI differs from, for example, formal program translation, in that it primarily verifies the behaviour of the new mutant version by running both the new and the old software on test inputs and comparing their output and performance in order to see if the new software can still do what is wanted of the original program and is now better. Genetic improvement can be used to create multiple versions of programs, each tailored to be better for a particular use or for a particular computer. Genetic improvement can be used with multi-objective optimization to consider improving software along multiple dimensions or to consider trade-offs between several objectives, such as asking GI to evolve programs which trade speed against the quality of answers they give. Of course it may be possible to find programs which are both faster and give better answers. Mostly Genetic Improvement makes typically small changes or edits (also known as mutations) to the program's source code but sometimes the mutations are made to assembly code, byte codeor binary machine code. (Wikipedia).
Lecture: Linear Programming and Genetic Algorithms
We consider a number of more advanced optimization algorithms that include the genetic algorithm and linear programming for constrained optimization.
From playlist Beginning Scientific Computing
9.6: Genetic Algorithm: Improved Fitness Function - The Nature of Code
In this video I look at strategies for improving the genetic algorithm's fitness function to improve efficiency and accuracy. https://thecodingtrain.com/more/archive/nature-of-code/9-genetic-algorithms/9.6-improved-fitness-function.html 🕹️ p5.js Web Editor Sketch: https://editor.p5js.org/
From playlist Session 2 - Genetic Algorithms - Intelligence and Learning
9.1: Genetic Algorithm: Introduction - The Nature of Code
Welcome to part 1 of a new series of videos focused on Evolutionary Computing, and more specifically, Genetic Algorithms. In this tutorial, I introduce the concept of a genetic algorithm, how it can be used to approach "search" problems and how it relates to brute force algorithms. 🎥 Next
From playlist Session 2 - Genetic Algorithms - Intelligence and Learning
Stanford Course - Genetic Engineering & Biotechnology
Preview the online course: Genetic Engineering and Biotechnology (XGEN203) More info: http://geneticscertificate.stanford.edu/courses/genetic-engineering-and-biotechnology.php The co-evolution of genetic engineering and biotechnology in the last 30+ years has allowed for groundbreaking fi
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From playlist 10 - Biology
Genetic Engineering | Genetics | Biology | Don't Memorise
Genetic Engineering is carried out by manipulating the genetic material of organisms. It mainly focuses on making products that would help largely in the fields like Medicine, Agriculture, Health and thus make the world a better place live in. To learn about Genetic Engineering in detail,
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Continuous Genetic Algorithm - Part 1
This video is about Continuous Genetic Algorithm - Part 1
From playlist Optimization
Teach Astronomy - Computers and Evolution
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From playlist 26. Life on Earth
Muin Khoury on Precision Public Health in the Era of Precision Medicine
Muin J. Khoury MD, PhD, from the CDC Office of Public Health Genomics, delivers the keynote address at the 2015 Stanford Center for Population Health Sciences Annual Colloquium. He discusses some of the challenges of precision medicine, including how your zip code is more important for you
From playlist Stanford Population Health Sciences
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From playlist Stanford Population Health Sciences
11B. Networks 3: The Future of Computational Biology: Cellular, Developmental, Social,...
MIT HST.508 Genomics and Computational Biology, Fall 2002 Instructor: George Church View the complete course: https://ocw.mit.edu/courses/hst-508-genomics-and-computational-biology-fall-2002/ YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP61gaHWysmlYNeGsuUI8y5GV We jus
From playlist HST.508 Genomics and Computational Biology, Fall 2002
MUTANT MENU | The Ethics of Gene Editing
SUBSCRIBE to BrainCraft! 👉 http://ow.ly/rt5IE If you could, would you design your DNA? And should you be able to? Gene editing technologies, including CRISPR, have the potential to save lives and cure disease; but using them also comes with risk. In this documentary, I talk to experts ar
From playlist MUTANT MENU: A BrainCraft Documentary
Gill Bejerano: How cryptogenomics advances science and privacy simultaneously
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From playlist The Future of Everything
Breakthrough of the Year, 2007: Human Genetic Variation
Accompanying Science's year-end special issue, this video features Francis Collins of the NIH, David Altshuler of the Broad Institute, and Science's Liz Pennisi reviewing some of the work that led studies in human genetic variation to be tagged the top scientific story for 2007. Science (
From playlist Breakthrough of the Year
Noah Rosenberg: How biology is becoming more mathematical
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SDS 547: How Genes Influence Behavior — with Prof. Jonathan Flint
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Stanford Seminar - Building Computers from Bacteriophage, Drew Endy
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From playlist Engineering
Research talks by Nisheeth Vishno
Second Bangalore School on Population Genetics and Evolution URL: http://www.icts.res.in/program/popgen2016 DESCRIPTION: Just as evolution is central to our understanding of biology, population genetics theory provides the basic framework to comprehend evolutionary processes. Population
From playlist Second Bangalore School on Population Genetics and Evolution
Step by Step: Algorithms That Teach You Math
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