Evolutionary algorithms | Optimization algorithms and methods
In computational intelligence (CI), an evolutionary algorithm (EA) is a subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm. An EA uses mechanisms inspired by biological evolution, such as reproduction, mutation, recombination, and selection. Candidate solutions to the optimization problem play the role of individuals in a population, and the fitness function determines the quality of the solutions (see also loss function). Evolution of the population then takes place after the repeated application of the above operators. Evolutionary algorithms often perform well approximating solutions to all types of problems because they ideally do not make any assumption about the underlying fitness landscape. Techniques from evolutionary algorithms applied to the modeling of biological evolution are generally limited to explorations of microevolutionary processes and planning models based upon cellular processes. In most real applications of EAs, computational complexity is a prohibiting factor. In fact, this computational complexity is due to fitness function evaluation. Fitness approximation is one of the solutions to overcome this difficulty. However, seemingly simple EA can solve often complex problems; therefore, there may be no direct link between algorithm complexity and problem complexity. (Wikipedia).
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
9.10: Genetic Algorithm: Continuous Evolutionary System - The Nature of Code
In this video, I apply the Genetic Algorithm to an "Ecosystem Simulation", a system in which models biological life more closely, where elements live and die continuously evolving over time. 💻Code : https://github.com/CodingTrain/Rainbow-Code 🎥Previous video : https://youtu.be/Zy_obitkyO
From playlist Session 2 - Genetic Algorithms - Intelligence and Learning
Build a Heap - Intro to Algorithms
This video is part of an online course, Intro to Algorithms. Check out the course here: https://www.udacity.com/course/cs215.
From playlist Introduction to Algorithms
Greedy Algorithm | What Is Greedy Algorithm? | Introduction To Greedy Algorithms | Simplilearn
This video on the Greedy Algorithm will acquaint you with all the fundamentals of greedy programming paradigm. In this tutorial, you will learn 'What Is Greedy Algorithm?' with the help of suitable examples. And finally, you will also discover few important applications of greedy algorithm
From playlist Data Structures & Algorithms [2022 Updated]
9.2: Genetic Algorithm: How it works - The Nature of Code
In part 2 of this genetic algorithm series, I explain how the concepts behind Darwinian Natural Selection are applied to a computational evolutionary algorithm. 🎥 Previous video: https://youtu.be/9zfeTw-uFCw?list=RxTfc4JLYKs&list=PLRqwX-V7Uu6bJM3VgzjNV5YxVxUwzALHV 🎥 Next video: https://yo
From playlist Session 2 - Genetic Algorithms - Intelligence and Learning
Algorithms Explained: What is an Algorithm?
This video defines what an algorithm is, distinguishes algorithms from recipes and functions and gives some examples of algorithms. This is the first video in an "Algorithms Explained" series that discusses algorithms at a conceptual level. Videos in this series that discuss specific algo
From playlist Algorithms Explained
The growth of evolutionary architecture - Interview with Rebecca Parsons
Rebecca Parsons, CTO at ThoughtWorks, explains what evolutionary architecture is, and the enablers that have allowed evolutionary architecture to grow. Parsons also discusses the difference between evolutionary and emergent architecture, and shares her thoughts on how she sees evolutionary
From playlist O'Reilly Software Architecture Conference 2016 - New York, New York
Evolution through Large Language Models
Install NLP Libraries https://www.johnsnowlabs.com/install/ Register for Healthcare NLP Summit 2023: https://www.nlpsummit.org/#register Watch all NLP Summit 2022 sessions: https://www.nlpsummit.org/nlp-summit-2022-watch-now/ Presented by Joel Lehman, Researcher at Stochastic Labs Th
From playlist NLP Summit 2022
Stanford Seminar - Computer-designed organisms - Josh Bongard
Josh Bongard University of Vermont April 22, 2020 View the full playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rMWw6rRoeSpkiseTHzWj6vu #computer #computers
From playlist Stanford EE380-Colloquium on Computer Systems - Seminar Series
POET: Paired Open-Ended Trailblazer | Paper Explained
❤️ Become The AI Epiphany Patreon ❤️ https://www.patreon.com/theaiepiphany 👨👩👧👦 Join our Discord community 👨👩👧👦 https://discord.gg/peBrCpheKE In this video I cover "Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and
From playlist Miscellaneous
PB2 - Population-Based Bandit Optimization
Notion Link: https://ebony-scissor-725.notion.site/Henry-AI-Labs-Weekly-Update-July-15th-2021-a68f599395e3428c878dc74c5f0e1124 Chapters 0:00 Introduction 2:41 Hyperparameter Optimization 3:44 Population-Based Training 6:12 Evolution + Bayesian Optimization 8:54 ASHA 10:48 Results Thanks
From playlist AI Weekly Update - July 15th, 2021!
AIUK: AI in Action (Session 4)
Chaired by Researcher and One HealthTech Co-founder, Maxine Mackintosh, this session will feature lively demonstrations from the UK’s leading AI researchers showcasing their work across a range of topics. Join the audience to put your questions to the researchers live. You will hear from:
From playlist AIUK 2021
#shorts An algorithm is a mathematical method of solving problems both big and small. #engineeringlexicon #algoritm #engineering #problem #mathematics Join our YouTube channel by clicking here: https://bit.ly/3asNo2n Find us on Instagram: https://bit.ly/3PM21xW Find us on Facebook: https
From playlist Engineering Lexicon
DEFCON 15: Revolutionizing the Field of Grey-box Attack Surface Testing with Evolutionary Fuzzing
Speakers: Jared DeMott Vulnerability Researcher Dr. Richard Enbody Associate Professor, Michigan State University Dr. Bill Punch Associate Professor, Michigan State University Runtime code coverage analysis is feasible and useful when application source code is not available. An evolut
From playlist DEFCON 15
Meta-Learning through Hebbian Plasticity in Random Networks (Paper Explained)
#ai #neuroscience #rl Reinforcement Learning is a powerful tool, but it lacks biological plausibility because it learns a fixed policy network. Animals use neuroplasticity to reconfigure their policies on the fly and quickly adapt to new situations. This paper uses Hebbian Learning, a bio
From playlist Papers Explained
Sriram Sankararaman: "Evolutionary Models in Population Genomics"
Computational Genomics Summer Institute 2016 "Evolutionary Models in Population Genomics" Sriram Sankararaman, UCLA Institute for Pure and Applied Mathematics, UCLA July 22, 2016 For more information: http://computationalgenomics.bioinformatics.ucla.edu/
From playlist Computational Genomics Summer Institute 2016
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
SDS 575: Optimizing Computer Hardware with Deep Learning — with Magnus Ekman
#NVIDIAComputerArchitecture #DeepLearning #HardwareMachineLearning In this episode, the Director of Architecture at NVIDIA, Dr. Magnus Ekman, joins Jon Krohn to discuss how machine learning, including deep learning, can optimize computer hardware design. The pair also review his exception
From playlist Super Data Science Podcast