This work addresses the signal propagation and the fractional-order dynamics during the evolution of a genetic algorithm (GA). In order to investigate the phenomena involved in the GA population evolution, the mutation is exposed to excitation perturbations during some generations and the corresponding fitness variations are evaluated. Three distinct fitness functions are used to study their influence in the GA dynamics. The input and output signals are studied revealing a fractional-order dynamic evolution, characteristic of a long-term system memory
A formalism for describing the dynamics of Genetic Algorithms (GAs) using methods from statistical m...
In order to study genetic algorithms in dynamic environments, we describe a stochastic finite popula...
A formalism is presented for modelling the evolutionary dynamics of a population of gene sequences. ...
This work addresses the signal propagation and the fractional-order dynamics during the evolution of...
This paper investigate the fractional-order dynamics during the evolution of a Genetic Algorithm (GA...
This work addresses the fractional-order dynamics during the evolution of a Genetic Algorithm popula...
This work addresses the signal propagation and the fractional-order dynamics during, the evolution o...
This work addresses the fractional-order dynamics during the evolution of a Genetic Algorithm popula...
AbstractMetastability is a common phenomenon. Many evolutionary processes, both natural and artifici...
Metastability is a common phenomenon. Many evolutionary processes, both natural and artificial, alte...
This paper is an introduction to the mathematical modelling of the dynamics of genetic algorithms. T...
A formalism is presented for modelling the evolutionary dynamics of a population of gene sequences. ...
A comparison is made between the dynamics of steady state and generational genetic algorithms using ...
This paper analyses the performance of a Genetic Algorithm (GA) in the synthesis of digital circuits...
We present a stochastic, finite population model of genetic algorithms in dynamic environments. In t...
A formalism for describing the dynamics of Genetic Algorithms (GAs) using methods from statistical m...
In order to study genetic algorithms in dynamic environments, we describe a stochastic finite popula...
A formalism is presented for modelling the evolutionary dynamics of a population of gene sequences. ...
This work addresses the signal propagation and the fractional-order dynamics during the evolution of...
This paper investigate the fractional-order dynamics during the evolution of a Genetic Algorithm (GA...
This work addresses the fractional-order dynamics during the evolution of a Genetic Algorithm popula...
This work addresses the signal propagation and the fractional-order dynamics during, the evolution o...
This work addresses the fractional-order dynamics during the evolution of a Genetic Algorithm popula...
AbstractMetastability is a common phenomenon. Many evolutionary processes, both natural and artifici...
Metastability is a common phenomenon. Many evolutionary processes, both natural and artificial, alte...
This paper is an introduction to the mathematical modelling of the dynamics of genetic algorithms. T...
A formalism is presented for modelling the evolutionary dynamics of a population of gene sequences. ...
A comparison is made between the dynamics of steady state and generational genetic algorithms using ...
This paper analyses the performance of a Genetic Algorithm (GA) in the synthesis of digital circuits...
We present a stochastic, finite population model of genetic algorithms in dynamic environments. In t...
A formalism for describing the dynamics of Genetic Algorithms (GAs) using methods from statistical m...
In order to study genetic algorithms in dynamic environments, we describe a stochastic finite popula...
A formalism is presented for modelling the evolutionary dynamics of a population of gene sequences. ...