Add to ORKGFinite state automata are crucial for numerous practical algorithms of computer science. We show how to use genetic algorithms and fuzzy automata to simplify a class of FSA defined by labeled graphs and considered in the literature
Abstract: GP (for Graph Programs) is a rule-based, nondeterministic program-ming language for solvin...
The author proposes an extension of genetic algorithm (GA) for solving fuzzy-valued optimization pro...
The author proposes an extension of genetic algorithm (GA) for solving fuzzy-valued optimization pro...
Finite state automata are crucial for numerous practical algorithms of computer science. We show how...
The genetic algorithm is described, including its three main steps: selection, crossover, and mutati...
Thesis (MSc (Computer Science))--University of Stellenbosch, 2006.The minimization of a Finite Autom...
Genetic Algorithms (GAS) are stochastic, non-derivative optimization method. They use popula-tions o...
The determinization of fuzzy automata is a well-studied problem in theoretical computer science cele...
optimization method. They use populations of acceptable solutions (genes) of the given problem, whic...
A methodology for the encoding of the chromosome of a genetic algorithm (GA) is described in the pap...
The thesis deals with the creation of an algorithm for the construction of the generalized finite au...
A significant part of digital circuits is constituted by sequen-tial synchronous circuits behaviour ...
A new method for applying grammar based Genetic Programming to learn fuzzy rule based classifiers fr...
We present the first (polynomial-time) algorithm for reducing a given deterministic finite state aut...
Symbolic finite automata (SFAs) are generalizations of classical finite state automata. Whereas the ...
Abstract: GP (for Graph Programs) is a rule-based, nondeterministic program-ming language for solvin...
The author proposes an extension of genetic algorithm (GA) for solving fuzzy-valued optimization pro...
The author proposes an extension of genetic algorithm (GA) for solving fuzzy-valued optimization pro...
Finite state automata are crucial for numerous practical algorithms of computer science. We show how...
The genetic algorithm is described, including its three main steps: selection, crossover, and mutati...
Thesis (MSc (Computer Science))--University of Stellenbosch, 2006.The minimization of a Finite Autom...
Genetic Algorithms (GAS) are stochastic, non-derivative optimization method. They use popula-tions o...
The determinization of fuzzy automata is a well-studied problem in theoretical computer science cele...
optimization method. They use populations of acceptable solutions (genes) of the given problem, whic...
A methodology for the encoding of the chromosome of a genetic algorithm (GA) is described in the pap...
The thesis deals with the creation of an algorithm for the construction of the generalized finite au...
A significant part of digital circuits is constituted by sequen-tial synchronous circuits behaviour ...
A new method for applying grammar based Genetic Programming to learn fuzzy rule based classifiers fr...
We present the first (polynomial-time) algorithm for reducing a given deterministic finite state aut...
Symbolic finite automata (SFAs) are generalizations of classical finite state automata. Whereas the ...
Abstract: GP (for Graph Programs) is a rule-based, nondeterministic program-ming language for solvin...
The author proposes an extension of genetic algorithm (GA) for solving fuzzy-valued optimization pro...
The author proposes an extension of genetic algorithm (GA) for solving fuzzy-valued optimization pro...