Many optimization problems in real world are dynamic in the sense that the global optimum value and the shape of fitness function may change with time. The task for the optimization algorithm in these environments is to find global optima quickly after the change in environment is detected. In this paper, we propose a new hybrid model of particle swarm optimization and cellular automata which a...

In adaptive resonance theory network, the choice of vigilance factor (VF) and choice parameter. (CP) affects the performance of the network, such as the number of classes into which the data are classified. These parameters are typically chosen and adapted using human judgment, experience, and heuristic rules. Rather than choosing and optimizing these parameters manually, we use learning automa...

Equational tree automata accept terms modulo equational theories, and have been used to model algebraic properties of cryptographic primitives in security protocols. A serious limitation is posed by the fact that alternation leads to undecidability in case of theories like ACU and that of Abelian groups, whereas for other theories like XOR, the decidability question has remained open. In this p...

Any weighted automaton (WA) defines a relation from finite words to values: given an input word, its set of values is obtained as the set of values computed by each accepting run on that word. A WA is k-valued if the relation it defines has degree at most k, i.e., every set of values associated with an input word has cardinality at most k. We investigate the class of quantitative languages defi...

Automata models of learning systems introduced in the 1960s were popularized as learning automata (LA) in a survey paper by Narendra and Thathachar (1974). Since then, there have been many fundamental advances in the theory as well as applications of these learning models. In the past few years, the structure of LA, has been modified in several directions to suit different applications. Concept...