EvoFlow - Evolutionary algorithms for humans
Install
pip install evoflow
Deep-learning versus Evoluationary algorithms
Generally you want to use Deep-learning when the problem is continious/smooth and evoluationary algorithms when the problem is discrete. For example voice generation is smooth and solving (non-linear) equations is discrete.
Terminology
- Gene: atomic unit. Equivalent to a neuron in deep-learning.
- Chromosome: ordered list of gene(s).
- Genotype: collection of chromosome(s). Used when the problem requires to maximizes multiples fitness function at once.
- Population of x: collection of chromosomes or genotypes. That is what makes a Tensor.
- Generation: One round of evolution. Equivalent to an epoch in deep-learning.
- Fitness function: Function that evaluate how good/fit a given chromosome is. this is equivalent to the loss function in deep learning except it doesn't need to be differentiable and aim to be maximized.
EvoFlow Terminology
-
evoluationary op: Operation performed on a population of chromosome to make them evolve. Common ops includes various type of Chromosomal crossovers and Chromosomal mutations. Equivalent to deep-learning layers (e.g a convolution layer).
-
evoluationary model: Directed graph of evolutionary ops used to evolve the population. Equivalent to a model architecture in deep-learning settings.
Disclaimer
This is not an official Google product.
1 maintainer
Metadata
1 maintainer
Release files for evoflow 0.3.1589576239
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| evoflow-0.3.1589576239.tar.gz | 32.0 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| evoflow-0.3.1589576239-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 108.2 kB
Release files / evoflow-0.3.1589576239.tar.gz
| Download URL | evoflow-0.3.1589576239.tar.gz |
|---|---|
| Size | 32.0 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
3f25c4c7de62f701fa4ccfc34e22c18760caed1004240a92dfe7d3de1fe874e2
|
|
BLAKE2b-256 checksum How to use checksums |
88f58e68ca60c09574fc370ff9d6a5a5b7b4c2768b8e8251f5bdc182a51636b8
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.1.1 pkginfo/1.5.0.1 requests/2.23.0 setuptools/41.2.0 requests-toolbelt/0.9.1 tqdm/4.46.0 CPython/3.8.2
|
Release files / evoflow-0.3.1589576239-py3-none-any.whl
| Download URL | evoflow-0.3.1589576239-py3-none-any.whl |
|---|---|
| Size | 76.1 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
7615f764c427386996fe16c5b6a94b1ae4de6af4e58f0d6828bd63f019530080
|
|
BLAKE2b-256 checksum How to use checksums |
b1703a794da7fb3a61960ec15051e062b138d86512cdbf843815f929010a22b8
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/3.1.1 pkginfo/1.5.0.1 requests/2.23.0 setuptools/41.2.0 requests-toolbelt/0.9.1 tqdm/4.46.0 CPython/3.8.2
|
