SamuelSchmidgall/EvolutionarySelfReplication

Produce intelligence by means of natural selection without objective/reward optimization

27
/ 100
Experimental

This project explores how complex behaviors can emerge without explicit goals or rewards, similar to natural evolution. It simulates organisms that learn to survive and self-replicate by interacting with their environment, rather than optimizing for a specific score. You observe how simple entities, represented by neural networks, adapt and evolve over generations based purely on their ability to stay 'alive' and create copies of themselves.

No commits in the last 6 months.

Use this if you are a researcher or student interested in understanding the fundamental mechanisms of intelligence and emergent behavior through evolutionary processes, particularly in the context of artificial life or AI theory.

Not ideal if you are looking for a practical AI tool to solve a specific problem with a predefined objective, like training a chatbot or optimizing a trading strategy.

Artificial-Life Evolutionary-Computation Emergent-Behavior AI-Theory Complex-Systems
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 6 / 25
Maturity 16 / 25
Community 5 / 25

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Stars

15

Forks

1

Language

Python

License

MIT

Last pushed

Sep 29, 2021

Commits (30d)

0

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