GATutor: a graphical tutorial system for genetic algorithms

TLDR: The design and implementation of GATutor, a graphical tutorial system for genetic algorithms (GA), and the set of help screens that explain, with examples, all of the options for each of the GA parameters are discussed.

Abstract: In this paper we discuss the design and implementation of GATutor, a graphical tutorial system for genetic algorithms (GA). The X Window/Motif system provides powerful tools for the development of a user interfaces with a familiar feel and look. We implemented the Traveling Salesman Problem (TSP) and the Set Covering Problem (SCP) as two example GA problems in the tutorial. The TSP problem uses an order-based chromosome representation (permutation of n objects), while the SCP uses bit strings. The user has numerous buttons to select the GA parameters. These include (a) type of initial population: random or from a file, (b) mode: steady-state or generational, (c) population size, (d) maximum number of generations or trials, (e) generation gap, (f) selection mode, (g) selection bias, (h) selection of the crossover operation from a choice of several possibilities, (i) mutation method, (j) mutation rate, (k) replacement method, (l), elitism, etc. The user has the ability to do astep by step execution or to do a continuous run. The screen layout provides visual representation of the chromosomes in the population with the ability to scroll. This gives the user the option of varying one or two GA parameters to visually see the effect on the algorithm. One of most important features of this tutorial is the set of help screens that explain, with examples, all of the options for each of the GA parameters. This package has already been very useful for teaching the fundamental features of GAs in many different courses, and it has been very valuable in our GA research projects.