Darrell Whitley
ABSTRACT
The U.S. Navy currently deploys Genetic Algorithms to schedule multi-resource satellites. We document this real-world application and also introduce a new synthetic test problem generator. A permutation is used as the representation. A greedy scheduler then converts the permutation into a schedule which can be displayed as a Gantt chart. Surprisingly, there have been few careful comparisons of standard generational Genetic Algorithms and Steady State Genetic Algorithms for these types of problems. In addition, this paper compares different crossover operators for the multi-resource satellite scheduling problem. Finally, we look at two ways of mapping the permutation to a schedule in the form of a Gantt chart. One method gives priority to reducing conflicts, while the other gives priority to reducing overlaps of conflicting tasks. This can produce very different results, even when the evaluation function stays exactly the same.
- L. Barbulescu, J.P. Watson, D. Whitley, and A. Howe. 2004. Scheduling Space-Ground Communications for the Air Force Satellite Control Network. Journal of Scheduling (2004).Google Scholar
- Laura Barbulescu, Jean-Paul Watson, L. Darrell Whitley, and Adele E. Howe. 2004. Scheduling Space-Ground Communications for the Air Force Satellite Control Network. J. Scheduling 7, 1 (2004), 7--34.Google ScholarDigital Library
- Laura Barbulescu, L. Darrell Whitley, and Adele E. Howe. 2004. Leap Before You Look: An Effective Strategy in an Oversubscribed Scheduling Problem.. In Proceedings of the 19th conference on Artificial Intelligence. 143--148.Google Scholar
- M.C. Carlisle and E.L. Lloyd. 1995. On the k-coloring of intervals. Discrete Applied Mathematics 59 (1995), 225--235.Google ScholarDigital Library
- T. Cormen, C. Leiserson, and R. Rivest. 1990. Introduction to Algorithms. McGraw Hill, New York.Google Scholar
- Lawrence Davis. 1985. Applying Adaptive Algorithms to Epistatic Domains. In Proc. IJCAI-85.Google Scholar
- Lawrence Davis. 1985. Job Shop Scheduling with Genetic Algorithms. In International Conference on Genetic Algorithms and their Applications.Google Scholar
- D. Goldberg. 1989. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading, MA.Google ScholarDigital Library
- D. Goldberg and K. Deb. 1991. A Comparative Analysis of Selection Schemes Used in Genetic Algorithms. In Foundations of Genetic Algorithms, G. Rawlins (Ed.). Vol. 1. Morgan Kaufmann, 69--93.Google Scholar
- David Goldberg and Jr. Robert Lingle. 1985. Alleles, Loci, and the Traveling Salesman Problem. In Int'l. Conf. on GAs and Their Applications, John Grefenstette (Ed.). 154--159.Google Scholar
- J. Holland. 1975. Adaptation in Natural and Artificial Systems. University of Michigan Press.Google ScholarDigital Library
- D. Parish. 1994. A Genetic algorithm approach to automating satellite range scheduling. Master's thesis. AFIT, Air Force Institute of Technology.Google Scholar
- T. Starkweather, S. McDaniel, K. Mathias, D. Whitley, and C. Whitley. 1991. A Comparison of Genetic Sequencing Operators. In Proc. of the 4th Int'l. Conf. on GAs, L. Booker and R. Belew (Eds.). Morgan Kaufmann, 69--76.Google Scholar
- Gilbert Syswerda. 1991. Schedule Optimization Using Genetic Algorithms. In Handbook of Genetic Algorithms, Lawrence Davis (Ed.). Van Nostrand Reinhold, New York, Chapter 21.Google Scholar
- Gilbert Syswerda and Jeff Palmucci. 1991. The Application of Genetic Algorithms to Resource Scheduling. In Proc. of the 4th Int'l. Conf. on GAs, L. Booker and R. Belew (Eds.). Morgan Kaufmann.Google Scholar
- Darrell Whitley, Timothy Starkweather, and D'ann Fuquay. 1989. Scheduling Problems and Traveling Salesmen: The Genetic Edge Recombination Operator. In Proc. of the 3rd Int'l. Conf. on GAs, J. D. Schaffer (Ed.). Morgan Kaufmann.Google Scholar
- L. Darrell Whitley and Nam-Wook Yoo. 1995. Modeling simple genetic algorithms for permutation problems. In Foundations of Genetic Algorithms (FOGA 3), Darrell Whitley and Michael Vose (Eds.). Morgan Kaufmann, 163--184.Google Scholar
- William J. Wolfe and Stephen E. Sorensen. 2000. Three Scheduling Algorithms Applied to the Earth Observing Systems Domain. Management Science 46, 1 (2000), 148--166. http://www.jstor.org/stable/2634914Google ScholarDigital Library
Index Terms
- Scheduling Multi-Resource Satellites using Genetic Algorithms and Permutation Based Representations
Recommendations
Genetic algorithm with genetic engineering technology for multi-objective dynamic job shop scheduling problems
GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computationGenetic algorithms were intensively investigated in various modifications and in combinations with other algorithms for solving the NP-hard scheduling problem. This extended abstract describes a genetic algorithm approach for solving large job shop ...
Genetic algorithms for task scheduling problem
The scheduling and mapping of the precedence-constrained task graph to processors is considered to be the most crucial NP-complete problem in parallel and distributed computing systems. Several genetic algorithms have been developed to solve this ...
A genetic algorithm for scheduling dual flow shops
This study examines a dual-flow shop-scheduling problem that allows cross-shop processing. The scheduling objective is to minimize the coefficient of variation of slack time (lateness), where the slack time (ST) of a job denotes the difference between ...
Comments