Introduction

GAGS is a C++ class library designed to make programming a Genetic Algorithm (GA) [Heitköeter and Beasley, 1996] easy, and at the same time, flexible enough to be added new objects, which are treated in the same way as native operators. As many other class libraries, GAGS includes:

• Chromosomes, which are the basic building blocks of a genetic algorithms. Chromosomes are bit strings, and have a variable length.
• Genetic operators, which are not part of the chromosome class, but are outside it; this way, operators are not reduced to mutation and crossover: there are 9 predefined genetic operators: bit-flip mutation, crossover (uniform, 1 and n-point, and gene-boundary respecting), creep (change gene value by 1), remove/reinsert, transpose (permute genes), and kill, remove, and randomAdd, which alter the length of the chromosome. New operators can be added and used in the same way as the predefined. Operators can be applied blindly, or to a certain part of a chromosome.
• Views, which represent the chromome and are used to evaluate and print it; views are objects used to watch the chromosome as a float or int array or whatever is needed to evaluate it.
• Population, which includes a list of chromosomes to be evaluated, sorts them when they are; includes operators for generational replacement, and a list of genetic operators together with their rates; the number of chromosomes, the list of operators and their rates can be changed in runtime.
• The evaluation or fitness function can be any C or C++ function, and it can return any class or type; thus, fitness is not reduced to a floating point number: it can be an array or any other user-defined type; the only requirement is that it has ordering operators defined.

The second and last feature set it apart from other GA class libraries, like Matthew's GAlib [Wall, 1995] and TOLKIEN [Tang, 1994]. At the same time, it lacks some features like an user interface and provision for more than one kind of chromosomes.

In this tutorial we will see the way a genetic algorithm is programmed using these building blocks. We will take a bottom-up approach, starting by a chromosome, and building up from there.