GENESIS v4.0

Generative Evolving Neural Engine for Self-Improving Systems

GENESIS is an experimental multi-agent evolution and learning simulation. The current genesis.py implementation combines neuroevolution, online adaptation, intrinsic-motivation signals, communication, counterfactual evaluation, shared knowledge, hierarchical goals, social prediction, concept formation, and self-narrative mechanisms in one self-contained Python program.

Project status: research prototype. GENESIS explores mechanisms associated with adaptive and self-improving systems; it is not evidence of artificial general intelligence or a formally verified Gödel machine.

Current capabilities

The v4 implementation contains 17 major capability areas:

#	Capability	Implementation focus
1	Passive learning	Agents update behavior from simulated experience
2	Meta-learning	Evolvable learning-rule parameters
3	Darwinian evolution	Mutation, crossover, topology change, and speciation
4	Validated self-modification	Candidate changes are evaluated before retention
5	Self-directed control	Agents choose actions from internal state and goals
6	Self-evaluation	Curriculum, diversity, and stagnation signals
7	Recovery mechanisms	Rollback and anomaly-handling paths
8	Intrinsic exploration	Curiosity, novelty search, and self-play-inspired signals
9	Open-ended behavior search	Evolution can discover unprogrammed behavior combinations
10	Decision intelligence	Causal memory, prediction, and temporal evaluation
11	Emergent communication	Evolvable signaling between nearby agents
12	Counterfactual reasoning	Alternative-action replay and regret-based adjustment
13	Persistent knowledge transfer	Shared knowledge survives individual agents
14	Hierarchical goal formation	Multi-level goals and sub-goal decomposition
15	Theory-of-mind approximation	Internal prediction models for other agents
16	Abstract concept formation	Prototype-based compression of repeated situations
17	Self-narrative	Compressed autobiographical state influencing later decisions

What changed in v4.0

v4 adds four capability families on top of the v3 communication, counterfactual, and shared-knowledge systems:

Hierarchical goal formation — agents can maintain goals and decompose them into smaller objectives.
Social prediction — agents model aspects of other agents' behavior to influence cooperation and competition.
Abstract concept formation — repeated experiences can be compressed into higher-level prototypes.
Self-narrative — agents maintain a compact history that can affect later decisions and inheritance.

Requirements

Python 3.11 or 3.12 recommended
NumPy
Matplotlib

Install dependencies from the repository manifest:

bash

python -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install -r requirements.txt

On Windows PowerShell, activate the environment with:

powershell

.venv\Scripts\Activate.ps1

Run

bash

python genesis.py

The default configuration currently runs 150 generations with 100 simulation steps per generation, so a full run is intentionally more substantial than a smoke test.

Verify core behavior

Run the deterministic fast test suite without starting the full simulation:

bash

python -m unittest discover -s tests -p "test_*.py" -v

The current core tests verify innovation-ID stability, minimal genome topology, finite bounded network activation, structural independence after genome copying, and learning-rule weight bounds.

Output

Generated plots are written under genesis_output/. The v4 visualization paths currently include:

genesis_output/genesis_v4_dashboard.png
genesis_output/genesis_v4_universe.png

Generated output and Python cache files are ignored by Git so experiments do not continuously add local artifacts to source control.

Continuous integration

The GENESIS quality workflow performs fast checks on pull requests and pushes that touch GENESIS:

compiles genesis.py and the core tests on Python 3.11 and 3.12;
installs the declared runtime dependencies;
verifies that NumPy and Matplotlib import successfully;
executes the deterministic GENESIS core behavioral test suite.

The workflow intentionally avoids running the full 150-generation simulation on every commit. Long experiment runs should be executed separately and their parameters/results recorded explicitly when used as evidence.

Architecture

See GENESIS_Architecture.md for the extended architecture notes. Where that document and the executable source disagree, treat genesis.py as the current implementation and open an issue or pull request to synchronize the documentation.

Reproducible research guidance

For comparable experiment results, record at minimum:

the Git commit SHA;
Python version and dependency versions;
random seed, when fixed;
configuration changes relative to Config;
number of generations and steps per generation;
the metric definition used for every reported result.

This separates observed experiment results from capability descriptions and makes future improvements easier to validate.

GENESIS v4.0 — an experimental platform for studying evolutionary, adaptive, social, and self-evaluating agent mechanisms.