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Two classes of dynamical recurrent neural networks, Continuous Time Recurrent Neural Networks (CTRNNs) (Yamauchi and Beer, 1994) and Plastic Neural Networks (PNNs) (Floreano and Urzelai, 2000) are compared on two behavioral tasks aimed at exploring their c ...
Device comprising storage means (51) for storing a genotypic representation (3) of a spiking neural network comprising spiking neurons (1) and input neurons (2) connected by synapses, and computer program portions for performing the steps of mutating said ...
We address two issues in Evolutionary Robotics, namely the genetic encoding and the performance criterion, also known as fitness function. For the first aspect, we suggest to encode mechanisms for parameter self-organization, instead of the parameters them ...
In this paper we investigate a novel approach to the evolutionary development of autonomous situated agents based on the assumption that the neural mechanisms underlying ontogenetic learning are themselves developed and shaped by evolutionary process. A ge ...
This thesis presents the development of a new multi-objective optimisation tool and applies it to a number of industrial problems related to optimising energy systems. Multi-objective optimisation techniques provide the information needed for detailed anal ...
The CEPA, an Evolutionary Algorithm that preserves diversity by finding clusters in the population, has had its convergence performance improved by a technique tentatively called ‘evolutionary operator selection’. Performance is compared to results found i ...
This paper is concerned with adaptation capabilities of evolved neural controllers. A method consisting of encoding a set of local adaptation rules that synapses obey while the robot freely moves in the environment [6] is compared to a standard fixed-weigh ...
This paper is concerned with adaptation capabilities of evolved neural controllers. We propose to evolve mechanisms for parameter self-organization instead of evolving the parameters themselves. The method consists of encoding a set of local adaptation rul ...
Morphology plays an important role in the computational properties of neural systems, affecting both their functionality and the way in which this functionality is developed during life. In computer-based models of neural networks, artificial evolution is ...
Sigmoidlike activation functions, as available in analog hardware, differ in various ways from the standard sigmoidal function because they are usually asymmetric, truncated, and have a non-standard gain. We present an adaptation of the backpropagation lea ...