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Jaco van Niekerk
South Africa
เข้าร่วมเมื่อ 29 เม.ย. 2022
Difference between global best (gbest) and local best (lbest) Particle Swarm Optimisation.
In this video the gbest (global best) and lbest (local best) PSO are demonstrated to find the maximum value of a generated Plasma. We also look at the predator-prey PSO and show how it can help get unstuck from local optimas.
มุมมอง: 219
วีดีโอ
Case studies for Evolutionary Algorithms (chromosome representation and fitness function)
มุมมอง 182ปีที่แล้ว
Case studies for Evolutionary Algorithms (chromosome representation and fitness function) Several case studies for representing the chromosome in Evolutionary Algorithms. 00:50 Evolving TEXT (Hello World of EAs) 06:22 The Knapsack problem 12:25 The Traveling Salesperson problem 23:33 Sudoku puzzles 28:43 Evolving equations 35:47 Digital Flies (Artificial Life)
Static routing and sub-netting explained in a single video
มุมมอง 176ปีที่แล้ว
Static routing and sub-netting explained in a single video
Lecture 1: Introduction to Linux; Virtual machine install
มุมมอง 200ปีที่แล้ว
Summary lecture that covers: - What is Linux - Boot process - Virtualisation and installation
Optimisation Lecture 10: Ant clustering algorithms
มุมมอง 4262 ปีที่แล้ว
In this video, we'll look at clustering, specicially the Lumer-Faieta algorithm.
Solving the 0-1 knapsack problem with the Binary PSO algorithm
มุมมอง 1.3K2 ปีที่แล้ว
In this (no-so-brief) video the Binary PSO is used to solve the 0-1 knapsack problem using good old Java.
Optimisation Lecture 9: Ant algorithms
มุมมอง 2872 ปีที่แล้ว
In this video, we'll look at the Simple Ant Colony Optimisation (SACO), the Ant System (AS) and the Ant Colony System (ACS) to find the shortest path in a grid (including Hamiltonian paths).
Optimisation Lecture 8: Particle Swarm Optimisation further enhancements!
มุมมอง 2932 ปีที่แล้ว
Further enhancements to the standard PSO. We look at multi-phase PSOs, multi-start PSOs, Repelling methods and various specific algorithms. The lecture is concluded withe the Binary PSO to solve discrete problems.
Optimisation Lecture 7 DEMO: PSO in Java
มุมมอง 4792 ปีที่แล้ว
A quick demonstration of velocity clamping and inertia weight on a gbest PSO.
Optimisation Lecture 7: Particle Swarm Optimisation enhancements!
มุมมอง 3832 ปีที่แล้ว
In this second video, we look at some of the enhancements that can be made to the basic PSO.
Optimisation Lecture 6: Particle Swarm Optimisation
มุมมอง 8852 ปีที่แล้ว
In this first video, we introduce the basics of PSOs. We look at the "gbest" and "lbest" algorithms and look at a practical example at the end of the video to get a feel for how the the algorithms work. Starling video: th-cam.com/video/U-Kb_0yEOjs/w-d-xo.html
Optimisation Lecture 5(b): Co-evolution
มุมมอง 2382 ปีที่แล้ว
Co-evolution uses competition or symbiosis to create novel solutions to problems. In this lecture we look at single-population and double-population techniques. Alpha Star video fragment th-cam.com/video/nbiVbd_CEIA/w-d-xo.html Research project Cameron R. Currie th-cam.com/video/R5piJCyHwtw/w-d-xo.html
Optimisation Lecture 5(a): Cultural Evolution/Algorithms
มุมมอง 7982 ปีที่แล้ว
Cultural Algorithms uses domain knowledge in a novel way to guide the search toward promising areas and steering away from less-optimal regions. When used correctly it can substantially speed-up the algorithm.
Optimisation Lecture 4: Differential Evolution
มุมมอง 6K2 ปีที่แล้ว
Differential Evolution uses a unique reproduction operator where the mutation rate is determined dynamically by exploiting the diversity of the population. This greatly enhances the ability of the algorithm to self-adjust parameters to find the optimal solution.
Optimisation Lecture 3(b): Evolutionary Programming
มุมมอง 7532 ปีที่แล้ว
Evolutionary Programming, a subset of Evolutionary Algorithms, uses mutation as the only reproduction operator. It has various unique ways in which the exploration-exploitation tradeoff is managed. In this lecture, the theory is covered in preparation of the two cases studies that will follow in another video.
Optimisation Lecture 3(a): Genetic Programming
มุมมอง 5612 ปีที่แล้ว
Optimisation Lecture 3(a): Genetic Programming
Optimisation Lecture 2: Genetic Algorithms
มุมมอง 7932 ปีที่แล้ว
Optimisation Lecture 2: Genetic Algorithms
Optimisation Lecture 1: An introduction to Evolutionary Algorithms.
มุมมอง 2.4K2 ปีที่แล้ว
Optimisation Lecture 1: An introduction to Evolutionary Algorithms.
Epic stuff man, simply Epic! One need only sit down for half an hour with your problem in mind to define a great template!
00:04 Ants organize corpses and larvae in clusters using individual behaviors. 02:16 Ants probability of dropping items based on certain constants and item frequency. 04:15 Ant clustering algorithm aims to maximize inter-cluster distances. 06:14 Calculating dissimilarity between clusters 08:17 Using density function to determine unique items 10:17 Explanation of Gamma values in Ant clustering algorithms 12:19 Ants drop items if density exceeds 0.3, pick up dissimilar items. 14:24 Optimizing ant clustering algorithms for efficient data clustering. Crafted by Merlin AI.
Nice explanation😇
excellent explanation
Kort en kragtig. Baie goed verduidelik, baie dankie :)
9:20 should the difference vector be the magnitude (i.e. always positive)?
No, you are adding vectors, not scalars thus you need to preserve the directional information to navigate through search space.
Is it possible to have the presentation file of this video?
thanks so much for this very explined information, I'm learning these key concepts.
Good revision video for my exam tomorrow, thank you🔥🙏🏾 Are you by any chance related to Prof Theo van Niekerk, who lectures at NMU? You sound slightly like him, and you give good explanations also! He taught me Mechatronic control systems
Probably... but I really don't know how as I do not know him. :-) We're all technically related. Thank you for the compliment.
Very interesting reminds me a book a read The Meme machine by Susan Blackmore. The Meme Machine follows through on Dawkins' (1976) fascinating suggestion that culture, like biology, evolves through the processes of variation, selection, and replication.
Quality Content!
Great video!
Is the target vector the same for each parent or do we use a specific trail vector for each parent?
A new target vector is chosen for each parent.
Can you solve this by taking any example with fitness function
Not sure I understand... but yes, you can use any fitness function.
Sir I was looking at Dominos {28 cards split 4 ways} is there a way to represent an EA that can take this information and represent a solution?🤔
I'm not completely sure I understand... you're welcome to contact my on skype (live:sparky_622) and we can discuss. It sounds interesting.
Hello, can you share book name?
Sorry, I missed this message. The book is: Computational Intelligence: An Introduction, Second Edition, Andries P. Engelbrecht
can you share me the related papers? thanks
Computational Intelligence: An Introduction Andries P. Engelbrecht
Thank you, I really wondered what that heckler looked like at 19:46
Sir, which book to use as a reference?
Computational Intelligence: An Introduction Andries P. Engelbrecht
can you help with Multi objective salp swarm algorith code in java ?
Hi can i get the source of the lecture or can i get the book i need it too
The text book is available from various online sites for free (just google). I will clean up the code and post at some point.
@@drjacovanniekerk Thank you, can you give me the full name of the book, because the name of the book is not clear to me?
@@israibrahim4160 Computational Intelligence: An Introduction 2nd Edition by Andries P. Engelbrecht
@@drjacovanniekerk Thank you so much
@@drjacovanniekerk Hello, I have some questions, can you help me?
Thanks for the vid! Doing this for a school assignment right now and your explanation was very helpful.
I am really glad. Good luck and keep on learning.
Thank you
Thank you sir for this presentation. I have one question, please Is DE/current to best is good for exploration or for exploitation or it offers a balance between the two?
Great question. Like most DE implementations it tries to strike a balance between the two. The difference vector between "best and parent" tries to explore more where the parent is weak (relative to best particle) and smaller, i.e. more exploitation where the parent the best particle are closer to one another.
@@drjacovanniekerk Could you please make another video explaining this point, please?
@@drjacovanniekerk So, can we say it is more exploitative?
@@drjacovanniekerk What if the best is far away to the global mimimum for example? For DE/current to best
@@drjacovanniekerk If they the parent and the best are far away to each other. Does it mean that DE/current to best is explorative?