Charles Prince
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.
- 1.A.L. Corcoran and R.L. Wainwright, "LibGA: A Userfriendly Workbench for Order-based Genetic Algorithm Research", Proceedings of the 1993 ACM/StGAPP Symposium on Applied Computing, pp. 111-118, 1993, ACM Press. Google Scholar
Digital Library
- 2.L. Davis, ed., Handbook of Genetic Algorithms, Van Nostrand Reinhold, 1991.Google Scholar
- 3.J.M. Fritz, "Hypercard Applications for Teaching information Systems", Proceedings of the Twenty- Second Technical Symposium on Computer Science Education, SIGCSE Bulletin Volume 23, Number 1, March, 1991, pp. 55-61. Google ScholarDigital Library
- 4.D.E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, 1989. Google ScholarDigital Library
- 5.L.R. Knight and R.L. Wainwright, "HYPERGEN: A Dis tributed Genetic Algorithm on a Hypercube", Proceedings of the 1992 Scalable High Performance Computing Conference, SHPCC'92, Williamsburg, Va., April 26-29, 1992.Google ScholarCross Ref
- 6.B.L. Kurtz, R.L. Oliver and E.M. Collins, "The Design, Implementation, and Use of DSTutor: A Tutorial System for Denotational Semantics", Proceedings of the Twenty-Second Technical Symposium on Computer Science Education, SIGCSE Bulletin Volume 23, Number 1, March, 1991, pp. 169-177. Google ScholarDigital Library
- 7.B.L. Lira and R. Hunter, "DBTooI: A Graphical Database Design Tool for an Introductory Database Course", Proceedings of the Twenty-Third Technical Symposium on Computer Science Education, SIGCSE Bulletin Volume 24, Number 1, March, 1992, pp. 24- 27. Google ScholarDigital Library
- 8.M. Newsome and C.M. Pancake, "A Graphical Computer Simulator for Systems Programming Courses", Proceedings of the Twenty-Third Technical Symposium on Computer Science Education, SIGCSE Bulletin Volume 24, Number 1, March, 1992, pp. 157- 162. Google ScholarDigital Library
- 9.G. Rawling, ed., Foundations of Genetic Algorithms, Morgan Kaufmann Publishers, 1991. Google ScholarDigital Library
- 10.D.A. Sekharan and R.L. Wainwright, "Manipulating Subpopulations of Feasible and Infeasible Solutions in Genetic Algorithms", Proceedings of the 1993 ACM/SIGAPP Symposium on Applied Computing, pp. 118-125, 1993, ACM Press. Google ScholarDigital Library
- 11.D. Schweitzer, "Designing Interactive Visualization Tools for the Graphics Classroom", Proceedings of the Twenty-Third Technical Symposium on Computer Science Education, SIGCSE Bulletin Volume 24, Number 1, March, 1992, pp. 299-303. Google ScholarDigital Library
Index Terms
- GATutor: a graphical tutorial system for genetic algorithms
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GATutor: a graphical tutorial system for genetic algorithms
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 ...
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