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Abstract: The
key technique of cellular automata operation is to establish transition
rules suitable to corresponding problem. Traditionally, the indirect
transition rules are established by experience or according to the
results obtained through other arithmetic. To overcome the shortcomings
of localization and long operation time caused by adopting indirect
transition rules, genetic algorithms based direct transition rules are
discussed. The direct local transition rules established by genetic
algorithms are used in the thin-plate topological layout minimum weight
optimization. Multi-objective optimization model is created and optimum
transition rules are obtained by the evolution of genetic algorithms.
From the simulation results, it is observed that the local direct rules
obtained by evolution are effective in the self-organization process of
the complex problems.
Key
words: Structural
topology optimization Cellular automata Genetic algorithm
Multi-objective optimization Finite element method (FEM)
CLC No: TH122
TP18 TP391.9
国家自然科学基金重大项目(50390060)和博士点基金(20020248048)资助项目.
Received 20040307, received in revised form 20020040720
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