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In this paper, an attractive approach for teaching genetic algorithm has been
presented. This approach is based primarily on using MATLAB in
implementing the genetic operators: crossover, mutation, and selection. An
advantage of using such an approach is that the student becomes familiar with
some advanced features of MATLAB, and furthermore, with the availability of
other MATLAB Toolboxes such as The Control Systems Toolbox, Neural
Network Toolbox and Fuzzy Logic Toolbox, it is possible for the student to
develop genetic algorithm-based approaches to designing intelligent systems,
which could lead to his/ her final year or MSc project.
presented. This approach is based primarily on using MATLAB in
implementing the genetic operators: crossover, mutation, and selection. An
advantage of using such an approach is that the student becomes familiar with
some advanced features of MATLAB, and furthermore, with the availability of
other MATLAB Toolboxes such as The Control Systems Toolbox, Neural
Network Toolbox and Fuzzy Logic Toolbox, it is possible for the student to
develop genetic algorithm-based approaches to designing intelligent systems,
which could lead to his/ her final year or MSc project.
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teaching-geentic-algorithms.pdf
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