coverage improvement is one of the main problems in wireless sensor networks. given a finite number of sensors, improvement of the sensor deployment will provide sufficient sensor coverage and save cost of sensors for locating in grid points. for achieving good coverage, the sensors should be placed in adequate places. this paper uses the genetic and learning automata as intelligent methods for...

Abstract We present new results on the computational limitations of affine automata (AfAs). First, we show that using endmarker does not increase power AfAs. Second, computation bounded-error rational-valued AfAs can be simulated in logarithmic space. Third, identify some logspace unary languages are recognized by algebraic-valued Fourth, arbitrary real-valued transition matrices and state vect...

In [18] the last three authors introduced the notion of a generalized directable automaton as a generalization of many already known types of automata. The algorithms for testing whether a finite automaton belongs to some of important subclasses of the class of generalized directable automata are studied by the authors in [20]. In this paper structural properties of finite and infinite generali...

Optimizing the database queries is one of hard research problems. Exhaustive search techniques like dynamic programming is suitable for queries with a few relations, but by increasing the number of relations in query, much use of memory and processing is needed, and the use of these methods is not suitable, so we have to use random and evolutionary methods. The use of evolutionary methods, beca...

Feature selection is of great importance in Quantitative Structure-Activity Relationship (QSAR) analysis. This problem has been solved using some meta-heuristic algorithms such as: GA, PSO, ACO, SA and so on. In this work two novel hybrid meta-heuristic algorithms i.e. Sequential GA and LA (SGALA) and Mixed GA and LA (MGALA), which are based on Genetic algorithm and learning automata for QSAR f...

Cellular learning automata is a combination of cellular automata and learning automata. The synchronous version of cellular learning automata in which all learning automata in different cells are activated synchronously, has found many applications. In some applications a type of cellular learning automata in which learning automata in different cells are activated asynchronously (asynchronous ...

Abstract We extend the $$\mathtt {L}^{\!\star }$$ L⋆ algorithm to learn bimonoids recognising pomset languages. then identify a class of automata that accepts precisely languages recognised by and show how convert between automata.

The cellular learning automata, which is a combination of cellular automata, and learning automata, is a new recently introduced model. This model is superior to cellular automata because of its ability to learn and is also superior to a single learning automaton because it is a collection of learning automata which can interact with each other. The basic idea of cellular learning automata, whi...

We present an exhaustive analysis of the problem of computing the relative entropy of two probabilistic automata. We show that the problem of computing the relative entropy of unambiguous probabilistic automata can be formulated as a shortest-distance problem over an appropriate semiring, give efficient exact and approximate algorithms for its computation in that case, and report the results of...