#GeneticAlgorithms

Gino MartorelliGert@poliversity.it
2026-01-10

Appunti di ricerca sulla evoluzione di dinamiche cooperative in società simulate con algoritmi genetici

In una società virtuale, composta da agenti artificiali che interagiscono secondo le regole del dilemma del prigioniero con iterazione, emergono complesse dinamiche e strategie di relazione.

Lo studio si ispira ai lavori di Robert Axelrod, un politologo della Università del Michigan che per primo sperimentò tornei tra automi capaci di giocare al Prisoner's Dilemma. Consentendo agli agenti artificiali di modificare la tabella di payoff di partenza, Genagents rappresenta una evoluzione di quelle sperimentazioni.

La sintesi del lavoro qui pubblicata si compone di una introduzione alla logica del Dilemma del Prigioniero con iterazione, una descrizione del modello matematico del sistema di simulazione preceduta da una breve introduzione alle teorie evoluzionistiche che lo hanno ispirato. Sono infine indicati i principali risultati sperimentali ottenuti e alcune riflessioni sui possibili scenari interpretativi in relazione a dinamiche osservabili nel mondo reale.

#ai #GeneticAlgorithms #GameTheory #sociology

jayah.net/rsc_genagents.html

2026-01-05
I came across a post on LinkedIn about evolutionary computation, and opted to post this in response:
I never stopped using evolutionary computation. I'm even weirder and use coevolutionary algorithms. Unlike EC, the latter have a bad reputation as being difficult to apply, but if you know what you're doing (e.g. by reading my publications 😉) they're quite powerful in certain application areas. I've successfully applied them to designing resilient physical systems, discovering novel game-playing strategies, and driving online tutoring systems, among other areas. They can inform more conventional multi-objective optimization.

Many challenging problems are not easily "vectorized" or "numericized", but might have straightforward representations in discrete data structures. Combinatorial optimization problems can fall under this umbrella. Techniques that work directly with those representations can be orders of magnitude faster/smaller/cheaper than techniques requiring another layer of representation (natural language for LLMs, vectors of real values for neural networks). Sure, given enough time and resources clever people can work out a good numerical re-representation that allows a deep neural network to solve a problem, or prompt engineer an LLM. But why whack at your problem with a hammer when you have a precision instrument?
I started to put up notes about (my way of conceiving) coevolutionary algorithms on my web site, here. I stopped because it's a ton of work and nobody reads these as far as I can tell. Sound off if you read anything there!

#AI #GenAI #GenerativeAI #LLMs #EvolutionaryComputation #GeneticAlgorithms #GeneticProgramming #EvolutionaryAlgorithms #CoevolutionaryAlgorithms #Cooptimization #CombinatorialOptimization #optimization
AI Daily Postaidailypost
2025-12-09

Discover how TPOT uses genetic algorithms to evolve machine‑learning pipelines in just four steps—crossover, mutation, grid search and more—on the classic Iris dataset. A concise guide for Python enthusiasts who want automated model building.

🔗 aidailypost.com/news/tpot-evol

Pragmatic Bookshelf 📚pragprog@techhub.social
2025-10-05

Genetic algorithms uncover solutions that brute force would miss, improving everything from shipping logistics to portfolio optimization.

Get Genetic Algorithms in Elixir by Sean Moriarity at pragprog.com/titles/smgaelixir
#elixir #geneticalgorithms #functionalprogramming

book cover and sale terms
2025-09-29

🚀 As the Google Summer of Code 2025 comes to a close, our two students write about their work, challenges and solutions. Mayn thanks for your hard work!!

Check out their final blog posts: blog.52north.org/category/gsoc/

👉 #KomMonitor: Breathing New Life into an Open-Source Gem (Pranjal Goyal)

👉 Genetic Algorithm for Ship Route Optimization (Shreyas Ranganatha)

#GSoC2025 #AngularMigration #geneticalgorithms

Chris Woody Woodruffcwoodruff
2025-09-18

🧬 Day 35, the final post of the Genetic Algorithms Bootcamp, is live!

Today: using GAs for creative art and design.
Evolution isn’t just for optimization. It can spark imagination, too.

Thanks to everyone who followed along, whether 1 post or all 35!

woodruff.dev/day-34-genetic-al

Chris Woody Woodruffcwoodruff
2025-09-17

🧬 Day 34 of the Genetic Algorithms Bootcamp is live!

Today, we compare GAs vs. other optimization techniques.

Where do GAs shine? Where do they fall short? A developer’s perspective.

woodruff.dev/day-34-genetic-al

Chris Woody Woodruffcwoodruff
2025-09-16

🧬 Day 33 of the Genetic Algorithms Bootcamp is live!

Case study: using GAs to optimize hyperparameters in a neural network.
Let evolution find better configs for smarter models.

woodruff.dev/day-33-case-study

Chris Woody Woodruffcwoodruff
2025-09-15

🧬 Day 32 of the Genetic Algorithms Bootcamp is live!

Today, we’re tackling when GAs go wrong.

From poor performance to premature convergence, learn how to debug and keep evolution on track.

woodruff.dev/day-32-when-genet

Chris Woody Woodruffcwoodruff
2025-09-12

🧬 Day 31 of the Genetic Algorithms Bootcamp is live!

Today, we’re talking about best practices for tuning GA parameters.

Mutation rate, crossover probability, population size… find the right balance for better results.

woodruff.dev/day-31-best-pract

Chris Woody Woodruffcwoodruff
2025-09-11

🧬 Day 30 of the Genetic Algorithms Bootcamp is live!

Today, we’re unit testing your evolution.

Make GAs in C# testable, predictable, and reliable.

woodruff.dev/day-30-unit-testi

Chris Woody Woodruffcwoodruff
2025-09-10

🧬 Day 29 of the Genetic Algorithms Bootcamp is live!

Today, we’re defining interfaces for GA components in C#: fitness, selection, and operators.

Clean, modular, and ready for evolution.

woodruff.dev/day-29-defining-i

Chris Woody Woodruffcwoodruff
2025-09-09

🧬 Day 28 of the Genetic Algorithms Bootcamp is live!

Today, we’re building a pluggable GA framework in C#.
Swap in operators, fitness functions, and configs like building blocks.

woodruff.dev/day-28-building-a

Chris Woody Woodruffcwoodruff
2025-08-21

🧬 Day 27 of the Genetic Algorithms Bootcamp is live!

Today we’re logging and monitoring GA progress.

Track fitness, spot stalls, and watch your code evolve generation by generation.

woodruff.dev/day-27-logging-an

Chris Woody Woodruffcwoodruff
2025-08-19

🧬 Day 26 of the Genetic Algorithms Bootcamp is live!

Today we’re running GAs in the cloud with Azure Batch or Functions.
Scale up, speed up, and let Azure handle the heavy lifting.

woodruff.dev/day-26-running-ga

Chris Woody Woodruffcwoodruff
2025-08-18

🧬 Day 25 of the Genetic Algorithms Bootcamp is live!

Today, we’re parallelizing GA loops in .NET with Parallel.ForEach

Evolve faster, scale bigger, and put those CPU cores to work.

woodruff.dev/day-25-scaling-up

Chris Woody Woodruffcwoodruff
2025-08-13

🧬 Day 24 of the Genetic Algorithms Bootcamp is live!

Today, we combine Genetic Algorithms + Hill Climbing.

A hybrid memetic approach for faster, smarter optimization in C#.

woodruff.dev/day-24-combining-

Chris Woody Woodruffcwoodruff
2025-08-12

🧬 Day 23 of the Genetic Algorithms Bootcamp is live!

Today, we dive into NSGA-II.
A powerful way to handle multiple objectives in your C# GA without losing diversity.

woodruff.dev/day-23-introducti

Chris Woody Woodruffcwoodruff
2025-08-11

🧬Day 22 of the Genetic Algorithms Bootcamp is live!

Today, we tackle multi-objective optimization.

When one fitness function isn’t enough, your GA learns to balance competing goals.

woodruff.dev/day-22-multi-obje

Chris Woody Woodruffcwoodruff
2025-08-05

🧬 Day 20 of the Genetic Algorithms Bootcamp is live!

Today, we’re penalizing bad solutions.

Learn how to handle constraints in your fitness function and guide your GA the right way.

woodruff.dev/day-20-constraint

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