Add to ORKGAbstract. The success of artificial neural network evolution is determined by many factors. One of these factors is the fitness function used in genetic algorithm. Fitness function determines selection pressure and Therefore influences the direction of evolution. It decides, whether received artificial neural network will be able to fulfill its tasks. Three fitness functions are proposed and examined in the paper, every one of them gives different selection pressure. Comparison and discussion of evolution results for every function is made.
The current state of machine learning algorithms is that they mostly rely on manually crafted design...
Evolutionary computation has been around ever since the late 50s. This thesis aims at elaborate on g...
In this paper we introduce a new generic selection method for Genetic Algorithms. The main differenc...
Inden B, Jin Y, Haschke R, Ritter H, Sendhoff B. An examination of different fitness and novelty bas...
In the present work, we study possibilities of using artificial neural networks for accelerating of ...
Learning and evolution are two fundamental forms of adaptation. There has been a great interest in c...
Abstract. To reduce the number of expensive fitness function evaluations in evolutionary optimizatio...
Genetic algorithms are most commonly applied to neural networks to determine their architecture or l...
This paper presents my work on an implementation of an Artificial Neural Network trained with a Gene...
Neuro-genetic systems, a particular type of evolving systems, have become a very important topic of ...
Genetic algorithms have been used to evolve several neural network architectures. In a previous effo...
This paper highlights the role of new Evolutionary Algorithm (EA) in designing Artificial Neural Net...
evolutionary algorithms, evolutionary strategies. evolutionary programming, genetic programming, neu...
There is a tremendous interest in the development of the evolutionary computation techniques as they...
In this paper, we apply genetic algorithms to the automatic generation of neural networks as well as...
The current state of machine learning algorithms is that they mostly rely on manually crafted design...
Evolutionary computation has been around ever since the late 50s. This thesis aims at elaborate on g...
In this paper we introduce a new generic selection method for Genetic Algorithms. The main differenc...
Inden B, Jin Y, Haschke R, Ritter H, Sendhoff B. An examination of different fitness and novelty bas...
In the present work, we study possibilities of using artificial neural networks for accelerating of ...
Learning and evolution are two fundamental forms of adaptation. There has been a great interest in c...
Abstract. To reduce the number of expensive fitness function evaluations in evolutionary optimizatio...
Genetic algorithms are most commonly applied to neural networks to determine their architecture or l...
This paper presents my work on an implementation of an Artificial Neural Network trained with a Gene...
Neuro-genetic systems, a particular type of evolving systems, have become a very important topic of ...
Genetic algorithms have been used to evolve several neural network architectures. In a previous effo...
This paper highlights the role of new Evolutionary Algorithm (EA) in designing Artificial Neural Net...
evolutionary algorithms, evolutionary strategies. evolutionary programming, genetic programming, neu...
There is a tremendous interest in the development of the evolutionary computation techniques as they...
In this paper, we apply genetic algorithms to the automatic generation of neural networks as well as...
The current state of machine learning algorithms is that they mostly rely on manually crafted design...
Evolutionary computation has been around ever since the late 50s. This thesis aims at elaborate on g...
In this paper we introduce a new generic selection method for Genetic Algorithms. The main differenc...