The intrinsic system model of the simple genetic algorithm with α-selection, uniform crossover and bitwise mutation

Authors:
André Neubauer
Affiliations:
Information Processing Systems Lab, Department of Electrical Engineering and Computer Science, Münster University of Applied Sciences, Steinfurt, Germany
Venue:
ICS'10 Proceedings of the 14th WSEAS international conference on Systems: part of the 14th WSEAS CSCC multiconference - Volume II
Year:
2010

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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 α-selection allows the analytical calculation of the unique fixed point of the corresponding intrinsic system model. In this paper, an overview of the theoretical results for the simple genetic algorithm with α-selection and its intrinsic system model is given. In addition to the theoretical analysis experimental results for the simple genetic algorithm with α-selection, uniform crossover and bitwise mutation are presented.