Finding Minimum Vertex Covering in Stochastic Graphs: A Learning Automata Approach

Computational Intelligence

2017

Self Cite

One of the problems with traditional genetic algorithms (GAs) is premature convergence, which makes them incapable of finding good solutions to the problem. The memetic algorithm (MA) is an extension of the GA. It uses a local search method to either accelerate the discovery of good solutions, for which evolution alone would take too long to discover, or reach solutions that would otherwise be unreachable by evolution or a local search method alone. In this paper, we introduce a new algorithm based on learning automata (LAs) and an MA, and we refer to it as LA‐MA. This algorithm is composed of 2 parts: a genetic section and a memetic section. Evolution is performed in the genetic section, and local search is performed in the memetic section. The basic idea of LA‐MA is to use LAs during the process of searching for solutions in order to create a balance between exploration performed by evolution and exploitation performed by local search. For this purpose, we present a criterion for the estimation of success of the local search at each generation. This criterion is used to calculate the probability of applying the local search to each chromosome. We show that in practice, the proposed probabilistic measure can be estimated reliably. On the basis of the relationship between the genetic section and the memetic section, 3 versions of LA‐MA are introduced. LLA‐MA behaves according to the Lamarckian learning model, BLA‐MA behaves according to the Baldwinian learning model, and HLA‐MA behaves according to both the Baldwinian and Lamarckian learning models. To evaluate the efficiency of these algorithms, they have been used to solve the graph isomorphism problem. The results of computer experimentations have shown that all the proposed algorithms outperform the existing algorithms in terms of quality of solution and rate of convergence.

Section: Learning Automatamentioning

confidence: 99%

Section: Learning Automatamentioning

confidence: 99%

Balancing exploration and exploitation in memetic algorithms: A learning automata approach

Mirsaleh

Computational Intelligence

2017

Self Cite

“…6 A variable action set learning automaton is an automaton with m actions in which the number of available actions at each instant changes with time. 6 A variable action set learning automaton is an automaton with m actions in which the number of available actions at each instant changes with time.…”

Section: Variable Set Action Learning Automatamentioning

confidence: 99%

“…The content of this section is from Rezvanian and Meybodi. 6 A variable action set learning automaton is an automaton with m actions in which the number of available actions at each instant changes with time. 10 Let a = {a 1 , a 2 , …, a m } to be a finite set of all actions.…”

Section: Variable Set Action Learning Automatamentioning

confidence: 99%

A new learning automata based approach for increasing utility of service providers

Morshedlou

2017

Int J Communication

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Utility is an important factor for serviceproviders, and they try to increase their utilities through adopting different policies and strategies. Because of unpredictable failures in systems, there are many scenarios in which the failures may cause random losses for service providers. Loss sharing can decrease negative effects of unexpected random losses. Because of capabilities of learning automata in random and stochastic environments, in this paper, a new learning automaton based method is presented for loss sharing purpose. It is illustrated that the loss sharing can be useful for service providers and helps them to decrease negative effect of the random losses. The presented method can be used especially in collaborative environments such as federated clouds.Results of the conducted experiments show the usefulness of the presented approach to improve utility of service providers.

“…Since the introduction of DLA by Beigy and Meybodi [14], DLA has been applied for various applications such as vehicle routing problem [59], stochastic graph problems [15,[60][61][62][63], wireless sensor networks [64], web mining [65], complex networks [7], social network analysis [66], and grid computing [67].…”

Section: Distributed Learning Automatamentioning

confidence: 99%

A new learning automata‐based sampling algorithm for social networks

Rezvanian

2015

Int J Communication

Self Cite

Recently, studying social networks plays a significant role in many applications of social network analysis, from the studying the characterization of network to that of financial applications. Due to the large data and privacy issues of social network services, there is only a limited local access to the whole network data in a reasonable amount of time. Therefore, network sampling arises to studying the characterization of real networks such as communication, technological, information, and social networks. In this paper, a sampling algorithm for complex social networks that is based on a new version of distributed learning automata (DLA) reported recently called extended DLA (eDLA) is proposed. For evaluation purpose, the eDLA-based sampling algorithm has been tested on several test networks and the obtained experimental results are compared with the results obtained for a number of well-known sampling algorithms in terms of relative error and Kolmogorov-Smirnov test. It is shown that eDLA-based sampling algorithm outperforms the existing sampling algorithms. Experimental results further show that the eDLA-based sampling algorithm in comparison with the DLA-based sampling algorithm has a 26.93% improvement for the average of Kolmogorov-Smirnov value for degree distribution taken over all test networks. Therefore, researchers try to study and analyze real networks such as communication, technological, information and OSNs via some metrics (i.e., centrality measures) or some techniques (i.e., network sampling methods) to estimate the characterization of these networks. Besides, some hidden important properties of networks (e.g., user age distribution, user activities, user connectivity, and many more in OSN) are discovered by studying on small sampled data. It is noted that sometimes sampling methods applied on the networks subject to preserve vital properties of the networks to reach a suitable visualization.A social network can be represented as a graph with a set of nodes such as users and a certain type of relationship between users as edges of graph. Let G = hV, Ei be a given social network graph, where V is the set of nodes and E is the set of edges. Sampled network G s = hV s , E s i is the induced subgraph of G by sampling rate ϕ, where V s ⊆ V, E s ⊆ E and 0 < ϕ < 1. It is noted that the main goal of sampling algorithms is to achieve a smaller subgraph similar to the original graph G. Sampling algorithms [8-10] play an important role in preprocessing, characterizing, studying, and analyzing the real networks. Network sampling can be applied for characterization of a small portion of a network subject to preserve main properties of the original network. For example, due to privacy restrictions of some OSN services, accessibility to the whole network information is not practical, therefore, the network is studied and characterized by estimated small samples from an important part of the original network. Taking into account large size, complexity, and dynamic properties of such real networks, most classic...