By Jun Wang, Andrew Kusiak
Regardless of the big quantity of guides dedicated to neural networks, fuzzy common sense, and evolutionary programming, few deal with the functions of computational intelligence in layout and production. Computational Intelligence in production Handbook fills this void because it covers the latest advances during this zone and cutting-edge functions. This finished guide comprises a good stability of tutorials and new effects, that enables you to
Manufacturing functions play a number one position in development, and this guide offers a prepared connection with advisor you simply via those advancements.
Read Online or Download Computational Intelligence in Manufacturing Handbook (The Mechanical Engineering Handbook Series) PDF
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Extra resources for Computational Intelligence in Manufacturing Handbook (The Mechanical Engineering Handbook Series)
The mutation operator forces the algorithm to search new areas. Eventually, it helps the GA to avoid premature convergence and find the global optimal solution. 9. 4 Inversion This operator is employed for a group of problems, such as the cell placement problem, layout problem, and travelling salesman problem. It also operates on one individual at a time. 10). 5 Control Parameters Important control parameters of a simple GA include the population size (number of individuals in the population), crossover rate, mutation rate, and inversion rate.
1997), An expert system to assist automatic remeshing in rigid plastic analysis, Toyota Technical Review, 46(2), 87-92. Zadeh L. , (1965), Fuzzy sets, Information Control, 8, 338-353. Zimmermann H. , Kluwer, Boston. 6 Introduction ....................................................................... 2-1 Modeling and Design of Manufacturing Systems ........... 2-3 Modeling, Planning, and Scheduling of Manufacturing Processes............................................................................
It is useful at the start of optimisation to discover promising regions in the search space. A low crossover frequency decreases the speed of convergence to such areas. If the frequency is too high, it can lead to saturation around one solution. The mutation operation is controlled by the mutation rate. A high mutation rate introduces high diversity in the population and might cause instability. On the other hand, it is usually very difficult for a GA to find a global optimal solution with too low a mutation rate.