REPOGEO REPORT · LITE

uber-research/deep-neuroevolution

Default branch master · commit 6ab22e19 · scanned 5/9/2026, 5:12:47 AM

GitHub: 1,663 stars · 300 forks

AI VISIBILITY SCORE

22 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

1 pass · 1 warn · 0 fail

Objective metadata checks

AI knows your name

1 / 3

Direct prompts that named your repo

HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface uber-research/deep-neuroevolution, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION

highreadme#1

Reposition README's opening to highlight core differentiator

Why:

CURRENT

## AI Labs Neuroevolution Algorithms

This repo contains distributed implementations of the algorithms described in:

[1] Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning

COPY-PASTE FIX

## Deep Neuroevolution: Genetic Algorithms for Deep Reinforcement Learning

This repository provides distributed implementations of neuroevolutionary algorithms, specifically demonstrating that **genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning**. It includes code for the algorithms described in:

[1] Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning

mediumabout#2

Refine repository description for clarity and differentiation

Why:

CURRENT

Deep Neuroevolution

COPY-PASTE FIX

Distributed implementations of neuroevolutionary algorithms, demonstrating genetic algorithms as a competitive alternative for training deep neural networks in reinforcement learning.

lowreadme#3

Clarify license information in the README

Why:

COPY-PASTE FIX

## License

This project includes a LICENSE file that outlines the terms of use. Please refer to the LICENSE file for specific details regarding permissions and limitations.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall

0 / 2

0% of queries surface uber-research/deep-neuroevolution

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

OpenAI ES

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

OpenAI ES · recommended 2×
DEAP · recommended 2×
Ray RLLib · recommended 1×
TensorFlow · recommended 1×
PyTorch · recommended 1×

CATEGORY QUERY
How can I train deep neural networks for reinforcement learning using evolutionary strategies?
you: not recommended
AI recommended (in order):
1. OpenAI ES
2. Ray RLLib
3. TensorFlow
4. PyTorch
5. DEAP
6. Nevergrad
AI recommended 6 alternatives but never named uber-research/deep-neuroevolution. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are distributed frameworks for applying neuroevolution to deep learning models?
you: not recommended
AI recommended (in order):
1. OpenAI ES
2. Ray
3. RLlib
4. DEAP
5. PyTorch-NEAT
6. TensorFlow-NEAT
7. Apache Spark
8. Dask
AI recommended 8 alternatives but never named uber-research/deep-neuroevolution. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
warn
Suggestion:
README presence
pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

Compared to common alternatives in this category, what is the core differentiator of uber-research/deep-neuroevolution?
pass
AI did not name uber-research/deep-neuroevolution — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts uber-research/deep-neuroevolution in production, what risks or prerequisites should they evaluate first?
pass
AI named uber-research/deep-neuroevolution explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
In one sentence, what problem does the repo uber-research/deep-neuroevolution solve, and who is the primary audience?
pass
AI did not name uber-research/deep-neuroevolution — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of uber-research/deep-neuroevolution. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/uber-research/deep-neuroevolution.svg)](https://repogeo.com/en/r/uber-research/deep-neuroevolution)

HTML

<a href="https://repogeo.com/en/r/uber-research/deep-neuroevolution"><img src="https://repogeo.com/badge/uber-research/deep-neuroevolution.svg" alt="RepoGEO" /></a>

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uber-research/deep-neuroevolution — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite