What are Genetic Algorithms?
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- Опубликовано: 7 июл 2024
- Welcome to a new series on evolutionary computation!
To start, we'll be introducing genetic algorithms - a simple, yet effective technique for solving difficult computational problems. We'll then visually demonstrate their use with a genetic maze solving simulation.
Source code
↪ Simulations written in Java using Processing.
↪ Genetic Camouflage: github.com/argonautcode/genet...
↪ Genetic Maze Solver: github.com/argonautcode/genet...
Socials
↪ Twitter: / argonautcode
Chapters
00:00 Intro
00:26 Biology
02:05 Genetic Camouflage
06:02 Genetic Maze-Solvers
10:00 Maze-Solvers, Take 2
11:50 Outro
just found this video while learning genetic algorithms for exams, and can't wait for the next video.😊
I am glad to have discovered this channel by chance!
A nice example for explaining local maxima problem.. watching this from the Algerian Sahara, keep uploading videos
Wow, it's been year since the last video !? Please keep 'em coming! I could swear that you would show the NEAT algorithm in the next video ? Please do it !
Amazing explanation!! Clear and very useful!! Thanks
That was just wonderful!
Thank you for your high-quality work
Biology meets Computer Science. This is so cool.
really interesting, looking forward to jump on the code,. It would be nice to have it in python instead of java but, any one has his own preferences! thanks for the video!
Absolutely loved the explanation on this one!
great video! keep up the good work!
I really like how this went from the absolute basics, until the rarely discussed greedy fitness problem
Thank you, I’m glad you liked the explanation!
Speaking of neuroevolution maze solver, would you test a maze that doesn't "cheat" by making the longest(highest entropy) path also the correct path(like the example maze in some novelty search papers)? Something like the deceptive tartarus environment perhaps? Though I reckon it might be a tough challenge better saved for later(or even bleeding edge AI research). Having multiple longest paths with only 1 correct answer might be a simpler approach
Very interesting thoughts! I'll be looking into simpler approaches first, as they are easier to explain, but I'll certainly want to revisit more advanced ideas in the future!
@@argonautcode you're welcome!
I see, guess the maze with multiple longest paths would me more suitable for that
underrated af
this is pretty well made!! thank you
Very cool visualizations. I liked it very much!
Really great video!
great work friend, thank you and keep it up please
wow amazing explanations and animations
Very cool video!
Amazing !
Sorry, where's the next episode?
I'd love if you shared how you made the visuals for this video, particularly what'd you use for the fitness function visualizer and statistics
Well explained.
Also, the local maximum problem could be solved by using BFS to compute the distance of every legal maze square to the exit, and using that distance as the fitness function. Right?
Yep, you could definitely use BFS!
i was using Genetic algorithm to tune a PID controller, my problem is that the values of Kp, Kd, Ki of the PID controller is in decimal values. how do i convert these decimals values to binary values and perform crossover and mutation.
I’m curious how to implement this problem with the visualisations.
Seeing is believing, and I’d love to make something like this to begin to comprehend it. Do you have any recommendations or planned tutorials on how we could create this maze problem?
Thanks. Subscribed!
More please.
I would say that more than inspired is an abstraction of the concept of evolution in biology and then it can be generalized in the realm of logic and information theory, starting with a set for population of autonomous agents, called P, and then each of these autonomous agents have information and then they have a process of recombination of this information. With any of these sets you can implement evolution. The obvious example in real life would be biological evolution, another example is ideas and the process of recombination is dialog and the equivalent to species would be culture.
can you open source the codes for us to experiment with it, codes for the visualize and how did you do such an amazing animations
Representation in a maze is harder than you initially think. I'm having trouble trying to do this. Basically how do you know how many genomes/moves an individual should have? Since they can't really ever grow in genome/move count?
While it’s a little tricky to grow move count, it’s not so difficult to lower it. For example, if an individual solves the maze with moves to spare, we can just ignore the remaining moves. So the idea is that we need a large enough move count so that the individuals have moves to spare. The exact number you pick for this would depend on your maze size and complexity, but it’s generally better to shoot high. We can then reduce the move count over time, relying on the genetic algorithm to pressure individuals to optimize their moves.
What if, instead of a single population whose fitness comes from both proximity and exploration, you split them into 2 subpopulations; explorers and improvers/exploiters. Each only gains fitness from its namesake. However, each generation the culled 50% in both subpopulations are filled children from both subpopulations, so the exploration and exploitation percolate
waiting for next video.
Maybe you should consider multiple species, rather than a single one