RRepoGEO

REPOGEO REPORT · LITE

MorvanZhou/Evolutionary-Algorithm

Default branch master · commit 9a9c7035 · scanned 5/20/2026, 8:03:08 AM

GitHub: 1,233 stars · 622 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
27 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 MorvanZhou/Evolutionary-Algorithm, 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 opening to explicitly state educational purpose

    Why:

    CURRENT
    In these tutorials, we will demonstrate and visualize algorithms like Genetic Algorithm, Evolution Strategy, NEAT etc.
    COPY-PASTE FIX
    This repository offers a comprehensive, beginner-friendly tutorial series and practical Python implementations for learning and visualizing key evolutionary algorithms such as Genetic Algorithm, Evolution Strategy, and NEAT.
  • mediumtopics#2
    Add more educational and example-focused topics

    Why:

    CURRENT
    distributed-es, es, evolution-strategies, evolution-strategy, evolutionary-algorithm, genetic-algorithm, machine-learning, microbial-ga, microbial-genetic-algorithm, neat, nes, neural-nets, neural-network, neuroevolution, openai, python, reinforcement-learning, travel-sale-problem, travel-sales-problem, tutorial
    COPY-PASTE FIX
    distributed-es, es, evolution-strategies, evolution-strategy, evolutionary-algorithm, genetic-algorithm, machine-learning, microbial-ga, microbial-genetic-algorithm, neat, nes, neural-nets, neural-network, neuroevolution, openai, python, reinforcement-learning, travel-sale-problem, travel-sales-problem, tutorial, learning, education, examples, course
  • lowreadme#3
    Clarify the relationship between this tutorial repo and the MEvo package

    Why:

    CURRENT
    A python package for Evolution algorithm is now available. Details are found at MEvo.
    COPY-PASTE FIX
    This repository focuses on providing educational tutorials and example implementations of evolutionary algorithms. For a dedicated, production-oriented Python package, please refer to the MEvo project.

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 MorvanZhou/Evolutionary-Algorithm
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
DEAP
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. DEAP · recommended 2×
  2. PyGAD · recommended 2×
  3. Platypus · recommended 1×
  4. SciPy · recommended 1×
  5. GEATpy · recommended 1×
  • CATEGORY QUERY
    How to implement genetic algorithms in Python for solving combinatorial optimization problems?
    you: not recommended
    AI recommended (in order):
    1. DEAP
    2. PyGAD
    3. Platypus
    4. SciPy
    5. GEATpy
    6. NumPy

    AI recommended 6 alternatives but never named MorvanZhou/Evolutionary-Algorithm. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking Python resources for learning and applying various evolution strategies in AI development.
    you: not recommended
    AI recommended (in order):
    1. DEAP
    2. PyTorch-ES
    3. evosax
    4. Nevergrad
    5. PyGAD
    6. ES-Py

    AI recommended 6 alternatives but never named MorvanZhou/Evolutionary-Algorithm. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 MorvanZhou/Evolutionary-Algorithm?
    pass
    AI did not name MorvanZhou/Evolutionary-Algorithm — 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 MorvanZhou/Evolutionary-Algorithm in production, what risks or prerequisites should they evaluate first?
    pass
    AI named MorvanZhou/Evolutionary-Algorithm 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 MorvanZhou/Evolutionary-Algorithm solve, and who is the primary audience?
    pass
    AI did not name MorvanZhou/Evolutionary-Algorithm — 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?

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MorvanZhou/Evolutionary-Algorithm — 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