SIMPLE GENETIC ALGORITHM WITH a-SELECTION - Intrinsic System Model, Fixed Points and the Fixed Point Graph
André Neubauer
2010
Abstract
Genetic algorithms (GA) are instances of random heuristic search (RHS) which mimic biological evolution and molecular genetics in simplified form. These random search algorithms can be theoretically described with the help of a deterministic dynamical system model by which the stochastic trajectory of a population can be characterized using a deterministic heuristic function and its fixed points. For practical problem sizes the determination of the fixed points is unfeasible even for the simple genetic algorithm (SGA). The recently introduced simple genetic algorithm with a-selection allows the analytical calculation of the unique fixed points of the dynamical system model. In this paper, an overview of the theoretical results for the simple genetic algorithm with a-selection and its corresponding intrinsic system model is given. Further, the connection to the fixed point graph is illustrated which describes the asymptotic behavior of the simple genetic algorithm. In addition to the theoretical analysis experimental results for the simple genetic algorithm with a-selection, uniform crossover and bitwise mutation are presented.
References
- Holland, J. H. (1992). Adaptation in Natural and Artificial Systems - An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. First MIT Press Edition, Cambridge.
- Neubauer, A. (2008a). Intrinsic system model of the genetic algorithm with a -selection. In Parallel Problem Solving from Nature PPSN X, Lecture Notes in Computer Science, pages 940-949. Springer.
- Neubauer, A. (2008b). Theory of genetic algorithms with a - selection. In Proceedings of the 1st IAPR Workshop on Cognitive Information Processing - CIP 2008, pages 137-141.
- Neubauer, A. (2008c). Theory of the simple genetic algorithm with a -selection. In Proceedings of the 10th Annual Genetic and Evolutionary Computation Conference - GECCO 2008, pages 1009-1016.
- Neubauer, A. (2009). Simple genetic algorithm with generalised a ?-selection. In Proceedings of the International Joint Conference on Computational Intelligence - IJCCI I2009, pages 204-209.
- Reeves, C. R. and Rowe, J. E. (2003). Genetic Algorithms - Principles and Perspectives, A Guide to GA Theory. Kluwer Academic Publishers, Boston.
- Vose, M. D. (1996). Modeling simple genetic algorithms. Evolutionary Computation, 3(4):453-472.
- Vose, M. D. (1999a). Random heuristic search. Theoretical Computer Science, 229(1-2):103-142.
- Vose, M. D. (1999b). The Simple Genetic Algorithm - Foundations and Theory. MIT Press, Cambridge.
- Vose, M. D. and Wright, A. H. (1998). The simple genetic algorithm and the walsh transform - part i, theory - part ii, the inverse. Evolutionary Computation, 6(3):253-273, 275-289.
Paper Citation
in Harvard Style
Neubauer A. (2010). SIMPLE GENETIC ALGORITHM WITH a-SELECTION - Intrinsic System Model, Fixed Points and the Fixed Point Graph . In Proceedings of the International Conference on Evolutionary Computation - Volume 1: ICEC, (IJCCI 2010) ISBN 978-989-8425-31-7, pages 281-288. DOI: 10.5220/0003113802810288
in Bibtex Style
@conference{icec10,
author={André Neubauer},
title={SIMPLE GENETIC ALGORITHM WITH a-SELECTION
- Intrinsic System Model, Fixed Points and the Fixed Point Graph},
booktitle={Proceedings of the International Conference on Evolutionary Computation - Volume 1: ICEC, (IJCCI 2010)},
year={2010},
pages={281-288},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003113802810288},
isbn={978-989-8425-31-7},
}
in EndNote Style
TY - CONF
JO - Proceedings of the International Conference on Evolutionary Computation - Volume 1: ICEC, (IJCCI 2010)
TI - SIMPLE GENETIC ALGORITHM WITH a-SELECTION
- Intrinsic System Model, Fixed Points and the Fixed Point Graph
SN - 978-989-8425-31-7
AU - Neubauer A.
PY - 2010
SP - 281
EP - 288
DO - 10.5220/0003113802810288