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...

with the explosive growth in amount of information, it is highly required to utilize tools and methods in order to search, filter and manage resources. one of the major problems in text classification relates to the high dimensional feature spaces. therefore, the main goal of text classification is to reduce the dimensionality of features space. there are many feature selection methods. however...

In this expository paper, we use automata for software model checking in a new way. The starting point is to fix the alphabet: the set of statements of the given program. We show how automata over the alphabet of statements can help to decompose the main problem in software model checking, which is to find the right abstraction of a program for a given correctness property.

Cellular learning automata is a combination of learning automata and cellular automata. This model is superior to cellular learning automata because of its ability to learn and also is superior to single learning automaton because it is a collection of learning automata which can interact together. In some applications such as image processing, a type of cellular learning automata in which the ...

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...

Developing and promoting self-directed learning, as an one of the goals in higher education is important because it expected from students to reach the high level of individual capabilities, and able to identify their training needs in order to fix it and evaluate their own learning, without being in a special education system. Conclusion: The literature review showed that self-directed learnin...