Genetic programming and data structures: genetic programming+ data structures= automatic programming!
WB Langdon - 1998 - books.google.com
Computers thatprogram themselves' has long been an aim of computer scientists. Recently
genetic programming (GP) has started to show its promise by automatically evolving …
genetic programming (GP) has started to show its promise by automatically evolving …
[BOOK][B] Genetic Programming: First European Workshop, EuroGP'98, Paris, France, April 14-15, 1998, Proceedings
W Banzhaf - 1998 - books.google.com
Evolutionary Computation (EC) holds great promise for computer science today. After an
early start in the 1950s, it was pursued by a handful of scientists until it took off as a rapidly …
early start in the 1950s, it was pursued by a handful of scientists until it took off as a rapidly …
A novel approach to design classifiers using genetic programming
We propose a new approach for designing classifiers for a c-class (c/spl ges/2) problem
using genetic programming (GP). The proposed approach takes an integrated view of all …
using genetic programming (GP). The proposed approach takes an integrated view of all …
Evolutionary fuzzy modeling
This study is concerned with a general methodology of identification of fuzzy models. Unlike
numeric models, fuzzy models operate at a level of information granules-fuzzy sets-and this …
numeric models, fuzzy models operate at a level of information granules-fuzzy sets-and this …
Constructive training of recurrent neural networks using hybrid optimization
Training of recurrent neural networks (RNNs) is known to be a very difficult task. This work
proposes a novel constructive method for simultaneous structure and parameter training of …
proposes a novel constructive method for simultaneous structure and parameter training of …
Evolving an edge selection formula for ant colony optimization
A Runka - Proceedings of the 11th Annual conference on Genetic …, 2009 - dl.acm.org
This project utilizes the evolutionary process found in Genetic Programming to evolve an
improved decision formula for the Ant System algorithm. Two such improved formulae are …
improved decision formula for the Ant System algorithm. Two such improved formulae are …
Genetically optimized logic models
This study is concerned with the genetic development of logic-based fuzzy models (logic
networks). The models are constructed with the aid of AND and OR fuzzy neurons. These …
networks). The models are constructed with the aid of AND and OR fuzzy neurons. These …
Multi-chromosomal CGP-evolved RNN for signal reconstruction
NM Khan, GM Khan - Neural Computing and Applications, 2021 - Springer
A multi-chromosomal structure for neuro-evolution of recurrent neural networks (RNNs) with
Cartesian genetic programming (CGP) architecture is presented to develop a signal …
Cartesian genetic programming (CGP) architecture is presented to develop a signal …
An evolutionary approach to training feedforward and recurrent neural networks
J Riley, VB Ciesielski - … Systems. Proceedings KES'98 (Cat. No …, 1998 - ieeexplore.ieee.org
This paper describes a method of utilising genetic algorithms to train fixed architecture
feedforward and recurrent neural networks. The technique described uses the genetic …
feedforward and recurrent neural networks. The technique described uses the genetic …
[PDF][PDF] Indexed bibliography of genetic algorithms and neural networks
JT Alander - University of Vaasa, Department of Information …, 1994 - researchgate.net
An Indexed Bibliography of Genetic Algorithms and Neural Networks Page 1 An Indexed
Bibliography of Genetic Algorithms and Neural Networks compiled by Jarmo T. Alander …
Bibliography of Genetic Algorithms and Neural Networks compiled by Jarmo T. Alander …