GAVis System Supporting Visualization, Analysis and Solving Combinatorial Optimization Problems Using Evolutionary Algorithms
The paper presents the GAVis (Genetic Algorithm Visualization) system designed to support solving combinatorial optimization problems using evolutionary algorithms. One of the main features of the system is tracking complex dependencies between parameters of an implemented algorithm with use of visualization. The role of the system is shown by its application to solve two problems: multiprocessor scheduling problem and Travelling Salesman Problem (TSP).
KeywordsGenetic Algorithm Evolutionary Algorithm Travel Salesman Problem Travel Salesman Problem Combinatorial Optimization Problem
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- 1.1. Collet P. (2001) EASEA - EAsy Specification for Evolutionary Algorithms. INRIA Ecole Polytechnique ENSTA.Google Scholar
- 3.3. Goldberg, David E. (1989) Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Pub. Co.Google Scholar
- 4.4. Knjazew D., (2002) OmeGA. A Competent Genetic Algorithm for Solving Permutation and Scheduling Problems, Kluwer Academic Publishers.Google Scholar
- 5.5. Merelo J.J., Castellano J.G. (2001) AI::EA (OPEAL) v0.3.Google Scholar
- 6.6. Schoenauer M. (2001) The Evolving Objects library tutorial.Google Scholar
- 7.7. Seredynski F., Zomaya A. (2002) Sequential and Parallel Cellular Automatabased Scheduling Algorithms, IEEE Trans. on Parallel and Distributed Systems, vol. 13.Google Scholar
- 8.8. Skaruz J., Seredynski F., Gamus M. (2004) Nature-inspired algorithms for the TSPGoogle Scholar
- 9.9. Wall M. (1996) GALib: A C++ Library of Genetic Algorithm ComponentsGoogle Scholar