A genetic algorithm framework grounded in biology.

Citation: Lim, JZR, Aw, ZQ, Goh, DJW, How, JA, Low, SXZ, Loo, BZL, Ling, MHT. 2010. A Genetic Algorithm Framework Grounded in Biology. The Python Papers Source Codes 2: 6.

Link to [Abstract], [PDF] and [Zipped Codes].

Here is a permanent link to this [PDF] and [Zipped codes] in my own archive.

This manuscript describes the implementation of a GA framework that uses biological hierarchy - from chromosomes to organisms to population.

Genetic algorithm (GA) is a heuristic search method inspired by biological evolution of genetic organisms by optimizing the genotypic combinations encoded within each individual with the help of evolutionary operators, such as reproduction, mutation and cross-over. This manuscript aims to present a simple GA framework written in Python programming language that conforms to biological hierarchy starting from gene to chromosome to genome (as organism) to population. Hence, we believe that this framework may be useful in both education and biological simulation on top of the usual domains where GA were used.