The culmination of this dissertation is to emulate nature\u27s design strategy in the evolution of neural networks. Specifically it is postulated that in order to capture the effectiveness of this mechanism (and avoid the commonly reported pitfalls or limitations), biologically inspired deviations from the general practice for evolving neural networks must be incorporated into the system. --Abstract, page iii
Neuroevolution, i.e. evolution of artificial neural networks, has recently emerged as a powerful tec...
Most contemporary connectionist approaches to AI use an Aritifical Neural Network (ANN) approach whi...
We really know of only a single intelligence abstraction approach that truly does work, the one base...
Hypotheses are presented of what could be specified by genes to enable the different functional arch...
Designing articial systems with ever more biologically-plausible `brains ' continues apace and ...
Capturing higher-order principles of evolution and neural function in a model may result in simulati...
It is hypothesised that one of the main reasons evolution has produced such a tremendous diversity o...
An ambitious long-term goal for neuroevolution, which studies how artificial evolutionary processes ...
An ambitious long-term goal for neuroevolution, which studies how artificial evolutionary processes ...
Abstract—An ambitious long-term goal for neuroevolution, which studies how artificial evolutionary p...
Evolutionary development as a strategy for the design of artificial neural networks is an enticing i...
Artificial neural networks are computational models of nervous systems. Natural organisms, however, ...
Abstract—Computational neuroscience uses networks of arti-ficial neurons to model cognitive function...
This dissertation clarifies the concept of evolvability, the increased capacity of some organisms or...
Evolution’s ability to find innovative phenotypes is an important ingredient in the emergence of com...
Neuroevolution, i.e. evolution of artificial neural networks, has recently emerged as a powerful tec...
Most contemporary connectionist approaches to AI use an Aritifical Neural Network (ANN) approach whi...
We really know of only a single intelligence abstraction approach that truly does work, the one base...
Hypotheses are presented of what could be specified by genes to enable the different functional arch...
Designing articial systems with ever more biologically-plausible `brains ' continues apace and ...
Capturing higher-order principles of evolution and neural function in a model may result in simulati...
It is hypothesised that one of the main reasons evolution has produced such a tremendous diversity o...
An ambitious long-term goal for neuroevolution, which studies how artificial evolutionary processes ...
An ambitious long-term goal for neuroevolution, which studies how artificial evolutionary processes ...
Abstract—An ambitious long-term goal for neuroevolution, which studies how artificial evolutionary p...
Evolutionary development as a strategy for the design of artificial neural networks is an enticing i...
Artificial neural networks are computational models of nervous systems. Natural organisms, however, ...
Abstract—Computational neuroscience uses networks of arti-ficial neurons to model cognitive function...
This dissertation clarifies the concept of evolvability, the increased capacity of some organisms or...
Evolution’s ability to find innovative phenotypes is an important ingredient in the emergence of com...
Neuroevolution, i.e. evolution of artificial neural networks, has recently emerged as a powerful tec...
Most contemporary connectionist approaches to AI use an Aritifical Neural Network (ANN) approach whi...
We really know of only a single intelligence abstraction approach that truly does work, the one base...