under an intensive competitive environment, the construction industry is facing pressure to meet the higher customer expectations under a tighter budget. on the other hand, construction is one of the main sectors generating greenhouse gases. according to published statistics, construction industry is one of the most important resources of greenhouse emissions in the world. therefore, based on t...

due to the algorithmic simplicity, cellular automata (ca) models are useful and simple methods in structural optimization. in this paper, a cellular-automaton-based algorithm is presented for simultaneous shape and topology optimization of continuum structures, using five-step optimization procedure. two objective functions are considered and the optimization process is converted to the single ...

This paper presents the VHDL implementation of fault tolerant cellular genetic algorithm. The goal of paper is to harden the hardware implementation of the cGA against single error upset (SEU), when affecting the fitness registers in the target hardware. The proposed approach, consists of two phases; Error monitoring and error recovery. Using innovative connectivity between processing elements ...

This paper presents a mathematical model for designing cellular manufacturing systems (CMSs) solved by genetic algorithms. This model assumes a dynamic production, a stochastic demand, routing flexibility, and machine flexibility. CMS is an application of group technology (GT) for clustering parts and machines by means of their operational and / or apparent form similarity in different aspects ...

Recurrent neural networks (RNNs), with the capability of dealing with spatio-temporal relationship, are more complex than feed-forward neural networks. Training of RNNs by gradient descent methods becomes more dii-cult. Therefore, another training method, which uses cellular genetic algorithms, is proposed. In this paper, the performance of training by a gradient descent method is compared with...

evolutionary algorithms are some of the most crucial random approaches tosolve the problems, but sometimes generate low quality solutions. on the otherhand, learning automata are adaptive decision-making devices, operating onunknown random environments, so it seems that if evolutionary and learningautomaton based algorithms are operated simultaneously, the quality of results willincrease sharpl...