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.
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Metadata
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Release files for evoflow 0.3.1589589257
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