Expand description
Overview
In general, the API of this library is still very much in development, but should be ready for experimental use.
Development using meiosis
Because this library heavily relies on type inference, development may sometimes trigger rustc
to suggest adding type annotations on some types. Ideally, this should not be necessary. It is
recommended to keep writing code and using it, till the compiler can figure out how all of the
genetic algorithm components fit together. Finally, once everything is in place, all of these
abstractions should have absolutely zero cost, as there is only one final implementation that
will run, which upholds all trait requirements and type invariants. In theory, having more
invariants known and checked for at compile time, we should end up with more efficient code.
Goals and Guarantees
Invalid States and Errors
When setting up an algorithm and for example choosing what Selection and Recombination
strategies to use, most if not all variables are known at compile time. This library tries to
leverage that as much as possible by using generics and traits that will only compile if the
basics are implemented and some sizes are given (like population size).
Should there be a variable that can only be known at runtime but was provided by the user, it
will be checked thanks to descriptive assert!s all over the code base. Over time, and with
each new version, a goal of this library is to replace runtime variables more and more by generic
replacements. Because of the nature of genetic algorithms it is imperative that const
environments are leveraged as much as possible to reduce runtime invariants, binary size, and
execution time.
Style and Target Audience
meiosis does not make assumptions about the knowledge of the user, which means that the code will
purposefully “look naive” in most places and it’ll have comments for any line of code that
requires advanced knowledge about why it does what it does.
In addition, there shall be no missing documentation, including on private items or trait implementations, strictly enforced by clippy. Because the topic of this library is so interwoven with biology any terminology used will also list aliases to make it easier for beginners to search through, no matter what field they come from.
If you have trouble to find something or have a hard time to understand a bit of code or how to use certain components, please do not hesitate to ask in an issue. You may also open a confident issue, should you want to include bits and pieces of production code to make issues reproducible.
Mistakes
This library is a product of a personal interest in the field of genetic algorithms and API design, so it might end up containing a few mistakes or bad phrasing. Please, if you are knowledgeable in this field or found an obvious error, do let me know in an issue!
I want this library to be an example for Rust beginners to learn from, and I try to maintain a high quality standard. If you, dear reader, have ideas, improvements or wish for a feature, please do not hesitate to open an issue. I would love to hear how people use this library and what I can do to ease their pain points with it.