Note to self:

A lot of this text was from an early version of the blog that I had made.  There might be something useful here, so I didn’t want to just throw it away.


A blog devoted to Evolutionary Computation, Evolvability and Artificial Intelligence (and anything else that interests me).


Here I walk through the design of a simple library for doing Evolutionary Computation (EC).  I’ll talk about the design decisions that I made in the process of creating the original LEAP library (now Eclypse? or maybe LEAPS or SLEAP?)  And by simple, I mean that I’ll reduce the number of advanced Python constructs that I use in order to make it more understandable for the average person.

I’ll also have a couple of very simple tutorials describing how to use Eclypse.  I’ll also use this library to explore some of the other things that I’m interested in, such as Evolvability, Evolutionary Rule Systems and Evolutionary Robotics.

Is Eclypse an acronym?  Maybe.  Maybe something like Evolutionary Computation Library Yielded from Python for Simple Examples.


I’ll describe some of the results of my dissertation, in which I developed some tools for diagnosing problem with an Evolutionary Algorithm.  In particular, these tools were built on the foundations of Quantitative Genetics and Price’s Theorem.


One specific area of interest to me is Evolvability, or that ability to evolve genetic codes that are themselves more adaptable.  The is very similar to the machine learning notion of “Learning to Learn”.