https://github.com/lukehollenback/genesis
Genetic algorithm that strives to breed perfect offspring from imperfect parents.
https://github.com/lukehollenback/genesis
bitwise-operations evolutionary-algorithms genetic-algorithms optimization-algorithms search-algorithms
Last synced: 5 months ago
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Genetic algorithm that strives to breed perfect offspring from imperfect parents.
- Host: GitHub
- URL: https://github.com/lukehollenback/genesis
- Owner: lukehollenback
- License: apache-2.0
- Created: 2020-02-10T01:44:19.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2020-09-17T03:36:27.000Z (over 5 years ago)
- Last Synced: 2024-06-21T03:14:44.110Z (almost 2 years ago)
- Topics: bitwise-operations, evolutionary-algorithms, genetic-algorithms, optimization-algorithms, search-algorithms
- Language: Go
- Homepage:
- Size: 23.4 KB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Genesis
A simple [genetic/evolutionary algorithm](https://en.wikipedia.org/wiki/Genetic_algorithm) written in Go.
Simulates the evolution of generations of creatures with varying traits through breeding,
mutation, and natural selection. The end goal is that a creature which has *all* known superior traits,
and *no* known inferior traits, is bred.
_As a note, this was a programming excercise – not a biology excercise. At best, this was a gross
simplification of reality fueled by
[casual reductionism](https://en.wikipedia.org/wiki/Fallacy_of_the_single_cause). At worst, it
was an excercise with no corrolation to reality at all._
Ultimately, this project serves to:
* Demonstrate use of many of Go's key language features.
* Demonstrate a (albeit simple) genetic algorithm at work.
## Example Output
```bash
$ go run .
--------------------------------------------------------------------------------
God = 00100110011111110011001010110011 (score = 32)
Adam = 11101100110100100010000100011101 (score = 15)
Eve = 01111011111111101001101010000001 (score = 19)
--------------------------------------------------------------------------------
Breeding next generation (1) (of 534 children)...
↝ Best Child = 11110100011101100011101000110001 (score = 23)
↝ Second Best Child = 11101111000111110011001010011001 (score = 23)
...Finished.
--------------------------------------------------------------------------------
Breeding next generation (2) (of 12 children)...
Naturally selecting with bad gene mask = 00000000000000000000000000000010...
↝ Best Child = 00110110000111010001101010010001 (score = 24)
↝ Second Best Child = 11101001011111110011101010111001 (score = 23)
...Finished.
--------------------------------------------------------------------------------
Breeding next generation (3) (of 96 children)...
↝ Best Child = 10100010001101110011001010110011 (score = 28)
↝ Second Best Child = 10100110011111010011101010110001 (score = 28)
...Finished.
--------------------------------------------------------------------------------
Breeding next generation (4) (of 337 children)...
↝ Best Child = 00100010011111110011001010110001 (score = 30)
↝ Second Best Child = 00100110011100110011001010110011 (score = 30)
...Finished.
--------------------------------------------------------------------------------
Breeding next generation (5) (of 756 children)...
Naturally selecting with bad gene mask = 00000000000000000000000000000100...
↝ Best Child = 00100110011111110011001010110011 (score = 32)
↝ Second Best Child = 00100110011101110011001010110011 (score = 31)
...Finished.
--------------------------------------------------------------------------------
Found best possible child after 5 generations.
Best Possible Child = 00100110011111110011001010110011 (score = 32)
```