Meta learning evolutionary artificial neural networks
نویسندگان
چکیده
منابع مشابه
Meta learning evolutionary artificial neural networks
In this paper, we present MLEANN (Meta-Learning Evolutionary Artificial Neural Network), an automatic computational framework for the adaptive optimization of artificial neural networks wherein the neural network architecture, activation function, connection weights; learning algorithm and its parameters are adapted according to the problem. We explored the performance of MLEANN and conventiona...
متن کاملEvolutionary reinforcement learning of artificial neural networks
In this article we describe EANT2, Evolutionary Acquisition of Neural Topologies, Version 2, a method that creates neural networks by evolutionary reinforcement learning. The structure of the networks is developed using mutation operators, starting from a minimal structure. Their parameters are optimised using CMA-ES, Covariance Matrix Adaptation Evolution Strategy, a derandomised variant of ev...
متن کاملMeta learning evolutionary arti!cial neural networks
In this paper, we present meta-learning evolutionary arti!cial neural network (MLEANN), an automatic computational framework for the adaptive optimization of arti!cial neural networks (ANNs) wherein the neural network architecture, activation function, connection weights; learning algorithm and its parameters are adapted according to the problem. We explored the performance of MLEANN and conven...
متن کاملEvolutionary Learning on Structured Data for Artificial Neural Networks
In this thesis, I present the GALSINE learning algorithm for automated learning on structured data. This is a genetic algorithm for artificial neural networks, modelled on evolution in nature, to allow effective machine learning while simultaneously limiting the amount of human intervention necessary. The performance of a machine learner depends on an appropriate knowledge representation for th...
متن کاملEvolving Artificial Neural Networks through Evolutionary Programming
Artiicial neural network (ANN) architecture design has been one of the most tedious and diicult tasks in ANN applications due to the lack of satisfactory and systematic methods of designing a near optimal architecture. Evolutionary algorithms have been shown to be very eeective in evolving novel ANN architectures for various problems. This paper proposes a new automatic method for simultaneousl...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Neurocomputing
سال: 2004
ISSN: 0925-2312
DOI: 10.1016/s0925-2312(03)00369-2