Add to ORKGThe genetic algorithm is described, including its three main steps: selection, crossover, and mutation. A comparison between implementation costs and running times of regular expressions matching a string is then made. The aim of this paper is to describe and analyze the behavior of an implementation of a non-deterministic finite-state acceptor using a genetic algorithm
Genetic algorithms (GAs) have several important features that predestinate them to solving logic syn...
An algorithm is given for determining if two finite automata with start states are equivalent. The a...
Although a lot of research has been done in the field of state-based testing, the automatic generati...
Abstract. Finite state machines play a key role in domains such as networking and natural language p...
Genetic Algorithms (GAS) are stochastic, non-derivative optimization method. They use popula-tions o...
Finite state automata are crucial for numerous practical algorithms of computer science. We show ...
optimization method. They use populations of acceptable solutions (genes) of the given problem, whic...
A significant part of digital circuits is constituted by sequen-tial synchronous circuits behaviour ...
Finite state automata are crucial for numerous practical algorithms of computer science. We show how...
Abstract. Evolutionary programming has originally been proposed for the breeding of nite state autom...
This bachelor’s thesis aims to examine possibilities of designing a transition function enabli...
This work presents an optimization method for the synthesis of finite state machines. The focus is o...
Evolutionary programming has originally been proposed for the breeding of finite state automata. The...
In the present work we deal with a branch of stochastic optimization algorithms, so called genetic a...
Presents a genetic algorithm used to infer pushdown automata from legal and illegal examples of a la...
Genetic algorithms (GAs) have several important features that predestinate them to solving logic syn...
An algorithm is given for determining if two finite automata with start states are equivalent. The a...
Although a lot of research has been done in the field of state-based testing, the automatic generati...
Abstract. Finite state machines play a key role in domains such as networking and natural language p...
Genetic Algorithms (GAS) are stochastic, non-derivative optimization method. They use popula-tions o...
Finite state automata are crucial for numerous practical algorithms of computer science. We show ...
optimization method. They use populations of acceptable solutions (genes) of the given problem, whic...
A significant part of digital circuits is constituted by sequen-tial synchronous circuits behaviour ...
Finite state automata are crucial for numerous practical algorithms of computer science. We show how...
Abstract. Evolutionary programming has originally been proposed for the breeding of nite state autom...
This bachelor’s thesis aims to examine possibilities of designing a transition function enabli...
This work presents an optimization method for the synthesis of finite state machines. The focus is o...
Evolutionary programming has originally been proposed for the breeding of finite state automata. The...
In the present work we deal with a branch of stochastic optimization algorithms, so called genetic a...
Presents a genetic algorithm used to infer pushdown automata from legal and illegal examples of a la...
Genetic algorithms (GAs) have several important features that predestinate them to solving logic syn...
An algorithm is given for determining if two finite automata with start states are equivalent. The a...
Although a lot of research has been done in the field of state-based testing, the automatic generati...