Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Theoretical Computer Science - Special issue on evolutionary computation
The Simple Genetic Algorithm: Foundations and Theory
The Simple Genetic Algorithm: Foundations and Theory
Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory
Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory
The simple genetic algorithm and the walsh transform: Part i, theory
Evolutionary Computation
Theory of the simple genetic algorithm with α-selection
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Intrinsic System Model of the Genetic Algorithm with α-Selection
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
An optimal robust digital image watermarking based on genetic algorithms in multiwavelet domain
WSEAS Transactions on Signal Processing
An efficient MIMO detection algorithm employed in imperfect noise estimation
WSEAS TRANSACTIONS on COMMUNICATIONS
Crossing genetic and swarm intelligence algorithms to generate logic circuits
WSEAS Transactions on Computers
Applications of genetic algorithms
WSEAS Transactions on Information Science and Applications
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
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Genetic algorithms (GA) are instances of random heuristic search (RHS) which mimic biological evolution and molecular genetics in simplified form. These random heuristic search algorithms can be theoretically described by an infinite population model 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) with fitness-proportional selection, crossover and bitwise mutation. 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. The unique fixed point of the intrinsic system model is derived and its compatibility with the equivalence relation imposed by schemata is shown. In addition to the theoretical analysis experimental results for the simple genetic algorithm with α-selection, uniform crossover and bitwise mutation are presented showing a close agreement to the theoretical predictions.