Many years ago, I wrote a program that generated fractal music using just intonation. You could select which one you liked the most, and it would apply a genetic algorithm based on your feedback to evolve ever cooler pieces. Now I'm seeing image generators that work on similar principles and it makes me want to dig out the old code.

#GeneticAlgorithms
#GeneticProgramming
#AlgorithmicComposition
#JustIntonation
#Fractals

📯 New blog post: "
Wavelength selection with a genetic algorithm"

If you're interested in #spectroscopy, especially NIR or Raman, or #chemometrics, a suitable selection of wavelength bands is often needed to produce a good statistical model.

Genetic algorithms are optimisation procedures loosely inspired by the mechanism of evolution by natural selection.

In this point, I'm working through an example of variable selection with a genetic algorithm for regression.

#MachineLearning #GeneticAlgorithms
#NearInfrared

https://nirpyresearch.com/wavelength-selection-genetic-algorithm/

I've published an article in DEV: "Exploring Genetic Algorithms with Ruby". I hope you enjoy it and found it useful! https://dev.to/rpaweb/exploring-genetic-algorithms-with-ruby-4lae

#ruby #rails #rubylang #rubyonrails #algorithms #genetics #geneticalgorithms #gen #crossover #mutation #elitism #development #webdev 🧬

I wish to say hello to everyone here. My name is Rafael, I'm a software developer and member of ACM since 2008.

These are some of my repositories:

https://github.com/rperez-rosario/AdaptiveComputingFramework

A minimal adaptive computing framework written in C#, can be used as a base for work on Genetic Algorithms.

https://github.com/rperez-rosario/HPCalculatorChess

A game of chess programmed using HP-PL on a HP Graphing Calculator.

#ACM #Mastodon #Introduction #GeneticAlgorithms #Chess

#introduction
hallo! I'm a software dev at a bank based in Regina SK with degrees in CS (my thesis was on ESG portfolio optimization using genetic algos) and propaganda film analysis (my thesis was on food representation in DPRK state sponsored media). I also used to play bass in a bunch of really awful bands and I love to cook

you can ask me anything about any of those things ~!
#softwaredev #computers #esg #economics #geneticalgorithms #propaganda #dprk #cooking #regina #saskatchewan

I fenomeni di emergenza in un sistema in evoluzione sembrano essere legati alla comparsa di processi dinamici nuovi rispetto alla loro qualità e imprevedibili sulla base della caratteristica iniziale del sistema. Per questo motivo è difficile modellizzare i fenomeni di emergenza perché il modello in uso può diventare improvvisamente inadeguato a delineare nuove manifestazioni dinamiche e quindi la vera identità del sistema.

In questa simulazione (nel primo video la registrazione di una sessione) ho cercato di costruire un metamodello che descriva alcuni fenomeni di emergenza durante l’evoluzione del sistema dinamico organismo-ambiente.
Usando algoritmi genetici ho realizzato un sistema di agenti artificiali che interagiscono risolvendo una versione spaziale del Dilemma del Prigioniero Iterato (Axelrod, 1984; Axelrod e Dion, 1988).

Il sistema, compresso tra i vincoli di una crescente complessità interna e l’impossibilità di scindersi, si mantiene spontaneamente entro valori di entropia che gli consentono di mantenere coerenza e “identità” nel tempo, permettendogli di far emergere diversi nuovi equilibri di Nash nella tabella dei payoff ( cultura? economia? politica?…) che regola gli scambi tra agenti artificiali; i nuovi equilibri di Nash emergenti nella tabella dei payoff facilitano l’emergere di alcuni “clan” che si replicano secondo le dinamiche genetiche del Cross-Over.

Per ogni generazione, le azioni del “clan vincente” contribuiscono a modificare la tabella dei payoff secondo regole più vantaggiose per la loro logica di interazione (cooperare/rifiutare/defezionare).

Ogni volta il nuovo equilibrio di Nash emergente viene mantenuto per alcune generazioni, oscillando tra diversi valori di entropia, finché un nuovo “clan” di agenti virtuali prevale sugli altri, orientando l’intero sistema verso l’emergere di un nuovo equilibrio di Nash nella tabella dei payoff.
#ai #geneticalgorithms #gametheory

I fenomeni di emergenza in un sistema in evoluzione sembrano essere legati alla comparsa di processi dinamici nuovi rispetto alla loro qualità e imprevedibili sulla base della caratteristica iniziale del sistema. Per questo motivo è difficile modellizzare i fenomeni di emergenza perché il modello in uso può diventare improvvisamente inadeguato a delineare nuove manifestazioni dinamiche e quindi la vera identità del sistema.

In questa simulazione ho cercato di costruire un metamodello che descriva alcuni fenomeni di emergenza durante l’evoluzione del sistema dinamico organismo-ambiente.
Usando algoritmi genetici ho realizzato un sistema di agenti artificiali che interagiscono risolvendo una versione spaziale del Dilemma del Prigioniero Iterato (Axelrod, 1984; Axelrod e Dion, 1988).

Il sistema, compresso tra i vincoli di una crescente complessità interna e l’impossibilità di scindersi, si mantiene spontaneamente entro valori di entropia che gli consentono di mantenere coerenza e “identità” nel tempo, permettendogli di far emergere diversi nuovi equilibri di Nash nella tabella dei payoff ( cultura? economia? politica?…) che regola gli scambi tra agenti artificiali; i nuovi equilibri di Nash emergenti nella tabella dei payoff facilitano l’emergere di alcuni “clan” che si replicano secondo le dinamiche genetiche del Cross-Over.

Per ogni generazione, le azioni del “clan vincente” contribuiscono a modificare la tabella dei payoff secondo regole più vantaggiose per la loro logica di interazione (cooperare/rifiutare/disertare).

Ogni volta il nuovo equilibrio di Nash emergente viene mantenuto per alcune generazioni, oscillando tra diversi valori di entropia, finché un nuovo “clan” di agenti virtuali prevale sugli altri, orientando l’intero sistema verso l’emergere di un nuovo equilibrio di Nash nella tabella dei payoff.
#ai #geneticalgorithms #gametheory

@Artisan_recycler When I was a kid, we had half an acre behind our house. I found some dried wild corn cobs my mom had used for decoration and scattered them in the lot. (She probably would've been mad had she known!) I didn't tend them. I just let them grow on their own. I was always fascinated by exactly what you're describing. It was basically a miniature experiment in evolution. I loved the thought of watching the corn adapt generation after generation to the local environment there.

I think about this when I mow the lawn, too. The very same species that might grow wild in the woods outside town grows differently in town because it has adapted to being repetitively mowed by either growing closer to the ground or recovering quickly from the trauma.

I've done a lot of #OpenEndedEvolution software simulations, and I'm a huge fan of #GeneticAlgorithms. All these things fascinate me for similar reasons. Nature and evolution are beautiful and mysterious.

Unleash the power of optimization with #GeneticAlgorithms! Check out my latest article on Genetic Algorithms for optimization problems.
#AI #MachineLearning
https://www.linkedin.com/pulse/genetic-algorithms-optimization-problems-sreenivas-bhattiprolu

Interesting article about D-Wave "#quantum" annealing computers. My niche in the aughts was "#scheduling" optimization (90% of the time it was bin-packing, technically). I started with #SimulatedAnnealing and #GeneticAlgorithms, but generally found approximation algorithms (Vazirani's book) to be most practical. PS It's a great niche. #SoftwareDevelopment

Hey there! I'm working on a seizure detection algorithm and I'm trying to decide which approach to take for selecting one solution from several in a n-dimensional space. I'm considering using a search algorithm like #GeneticAlgorithms or simulated annealing, or a #MachineLearning model like a decision tree or #NeuralNetwork. I'm wondering which approach would be best for selecting one solution among many options. Any advice or recommendations would be greatly appreciated!😊

Re: #SolidBrains and #GeneticAlgorithms, @ricard_sole@twitter.com outlines a history stretching back into the work of W. Pitts, J. J. Hopfield, S. A. Kauffman, and others to examine the origin of attractor neural network models — a.k.a., "An EXTREME simplification of the reality."

Finally! I adapt the implementation code of a paper from my project and now on #Github and #PapersWithCode :partyparrot:

The article that I implemented is about #neuralArchitectureSearch #neuralnets #geneticAlgorithms

https://github.com/ttiagojm/GP-CNAS

If you are a #scifi fan, #programmer, or #gamer, this blog post from way back in 2012 might interest you.

Topics include:
#artificialintelligence
#ArtificialLife
#geneticalgorithms
#cyberspace
#metaverse
#virtualreBuilding
#cyberdrome

Notes on Building an Artificial Life Simulation.
http://josephrhea.blogspot.com/2012/02/notes-on-building-artificial-life.html

What are the most used hashtags for genetic algorithms ? I saw few people talking about this interesting topic!

Those -> #EvolutionaryComputing #geneticalgorithms #geneticprogramming ?

Another example of (sexual) reproduction in my genetic algorithm! If you look closely, you can easily see how children (front) inherit traits from their parents (back).

The white box is a placeholder for the player. Can't wait to get to the point where a whole family of #creatures will let you know what they think of you raiding their #caves!

#WIP #gamedev #screenshots #procedural #procgen #genetics #geneticalgorithms #DNA #rpg

New people that follow me or that are seeing this toot, in what fields you apply/research #AI ?
For now I’m researching about CNAS (Convolution Neural Architecture Search) using #geneticalgorithms like I said some toots ago 😆.

And you ?

#Evolution over six generations.

The driving force was a target function rewarding long legs, which I think becomes pretty obvious after generation 3.

In each generation, I sorted #creatures by their score from highest (left) to lowest (right) and only let the two highest-scoring creatures reproduce (note the parent-child relation ships shown as green lines).

#geneticalgorithms #DNA #mutation #WIP #gamedev #indiedev #solodev

#arachnophobia

A nice review about genetic operators: https://link.springer.com/article/10.1007/s11042-020-10139-6

Probably the most important table is Table 6 showing the best combinations of them.

#geneticProgramming #geneticAlgorithms

Showing off family relationships!

Note how children resemble their parents, this is only possible because all creatures are created from DNA strings and the parent's DNA is mixed to create the children...

#genetics #geneticalgorithms #DNA #gamedev #blender3d #b3d #indiedev #mutations #evolution