A Mathematical Framework for Cellular Learning Automata

Beigy, Hamid; Meybodi, Mohammad Reza

doi:10.1142/s0219525904000202

Cited by 129 publications

(54 citation statements)

References 16 publications

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“…Proofs of these theorems can be found in [22]. If the CLA satisfies the sufficiency conditions needed for Theorem 1 and 2, then the CLA will converge to a compatible configuration; otherwise the convergence of CLA to a compatible configuration cannot be guaranteed, and it may exhibit a limit cycle behavior [23].…”

Section: Steady State Condition For Cellular Learning Automatamentioning

confidence: 99%

A Novel Stochastic Learning Automata Based SON Interference Mitigation Framework for 5G HetNets

Qureshi¹,

Tiwana²

2016

RADIOENGINEERING

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Section: Steady State Condition For Cellular Learning Automatamentioning

confidence: 99%

A Novel Stochastic Learning Automata Based SON Interference Mitigation Framework for 5G HetNets

Qureshi¹,

Tiwana²

2016

RADIOENGINEERING

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“…CLA has found many applications such as image processing [81], rumor diffusion [82], channel assignment in cellular networks [83] and VLSI placement [84], to mention a few. For more information about CLA the reader may refer to [85][86][87][88].…”

Section: Cellular Learning Automatamentioning

confidence: 99%

Deployment of a mobile wireless sensor network with k-coverage constraint: a cellular learning automata approach

Esnaashari

Meybodi

2012

Wireless Netw

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Deployment of a wireless sensor network is a challenging problem, especially when the environment of the network does not allow either of the random deployment or the exact placement of sensor nodes. If sensor nodes are mobile, then one approach to overcome this problem is to first deploy sensor nodes randomly in some initial region within the area of the network, and then let the sensor nodes to move around and cooperatively and gradually increase the covered section of the area. Recently, a cellular learning automata-based deployment strategy, called CLA-DS, is introduced in literature which follows this approach and is robust against inaccuracies which may occur in the measurements of sensor positions or in the movements of sensor nodes. Despite its advantages, this deployment strategy covers every point within the area of the network with only one sensor node, which is not enough for applications with k-coverage requirement. In this paper, we extend CLA-DS so that it can address the k-coverage requirement. This extension, referred to as CLA-EDS, is also able to address k-coverage requirement with different values of k in different regions of the network area. Experimental results have shown that the proposed deployment strategy, in addition to the advantages it inherits from CLA-DS, outperforms existing algorithms such as DSSA, IDCA, and DSLE in covering the network area, especially when required degree of coverage differs in different regions of the network.

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“…The CLA is synchronous if all cells are synchronized with a global clock and activated at the same time and it is uniform if each cell uses the same local rule. In this paper we use uniform asynchronous CLA [6].…”

Section: -Cellular Learning Automatamentioning

confidence: 99%

“…This model is superior to CA because of its ability to learn and also is superior to a single learning automaton because it is a collection of learning automata, which can interact with each other and solve a particular problem. The basic idea of CLA, which is a super class of stochastic CA, is to use learning automata to adjust the state transition probability of stochastic CA [6]. So far, CLA has been used in many applications including image processing [17] [21], VLSI placement [19], rumor diffusion [20], commerce networks [18], channel assignment in cellular mobile systems [7], call admission control in cellular mobile system [5], cooperation in mutiagent systems [15], load balancing in computational grid [10].…”

Section: Introductionmentioning

confidence: 99%

“…So far, CLA has been used in many applications including image processing [17] [21], VLSI placement [19], rumor diffusion [20], commerce networks [18], channel assignment in cellular mobile systems [7], call admission control in cellular mobile system [5], cooperation in mutiagent systems [15], load balancing in computational grid [10]. For more information about CLA the reader may refer to [6].…”

Section: Introductionmentioning

confidence: 99%

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A new fine-grained evolutionary algorithm based on cellular learning automata

Rastegar

Meybodi

Hariri

2006

HIS

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In this paper, a new evolutionary computing model, called CLA-EC, is proposed. This model is a combination of a model called cellular learning automata (CLA) and the evolutionary model. In this model, every genome in the population is assigned to one cell of CLA and each cell in CLA is equipped with a set of learning automata. Actions selected by learning automata of a cell determine the genome's string for that cell. Based on a local rule, a reinforcement signal vector is generated and given to the set of learning automata residing in the cell. On the basis of the received signal, each learning automaton in the cell updates its internal structure according to a learning algorithm. The process of action selection and updating the internal structure of learning automata is repeated until a predetermined criterion is met. To show the effectiveness of the proposed model it is used to solve several optimization problems such as real valued function optimization and clustering problems.

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A Mathematical Framework for Cellular Learning Automata

Cited by 129 publications

References 16 publications

A Novel Stochastic Learning Automata Based SON Interference Mitigation Framework for 5G HetNets

A Novel Stochastic Learning Automata Based SON Interference Mitigation Framework for 5G HetNets

Deployment of a mobile wireless sensor network with k-coverage constraint: a cellular learning automata approach

A new fine-grained evolutionary algorithm based on cellular learning automata

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