For the final installment in the Biologically Inspired Computation series, we’ll be taking a look at genetic algorithms.

Theory

From Wikipedia:

A genetic algorithm (GA) is a search technique used in computing to find exact or approximate solutions to optimization and search problems. Genetic algorithms are categorized as global search heuristics. Genetic algorithms are a particular class of evolutionary algorithms (also known as evolutionary computation) that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover (also called recombination).

I even took the liberty of including my paper on my findings with this project, just because there are so many parameters to test and creating the graphs can be rather time consuming. So take a look at the paper if you would like to read up on the relationships between the number of chromosomes and the average fitness of the population at any given generation, for example.

More uses of NSOperation and NSOperationQueue for multithreading in this project.

Download

Xcode Project (3.1 or later) | Leopard Only

Genetic Algorithm | Leopard Only

Biologically Inspired Computation Series