Neuroevolution: from architectures to learning

Floreano, Dario; Dürr, Peter; Mattiussi, Claudio

doi:10.1007/s12065-007-0002-4

Cited by 598 publications

(339 citation statements)

References 75 publications

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“…For example, neuroevolutionary approaches use EAs to optimise the structure, the parameters, or both simultaneously, of artificial neural networks 33,34 . In other branches of machine learning, using EC to design algorithms has been shown to be very effective as an alternative to hand-crafting them, for instance, for inducing decision-trees 35 .…”

Section: Applications Of Evolutionary Computationmentioning

confidence: 99%

From evolutionary computation to the evolution of things

Eiben¹,

Smith

2015

Nature

375

189

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Section: Applications Of Evolutionary Computationmentioning

confidence: 99%

From evolutionary computation to the evolution of things

Eiben¹,

Smith

2015

Nature

375

189

View full text Add to dashboard Cite

“…Our neuro-evolutionary algorithm, too, has been already tested and applied with success to several real-world problems, showing how such an approach can be useful in different classification problems, like automated trading strategy optimization [3,28], incipient fault diagnosis in electrical drives [29], automated diagnosis of skin diseases [30], etc. Further insights on the evolutionary optimization of ANNs can be found in some broad surveys on the topic [31,33,34].…”

Section: Neuro-evolutionary Classifiersmentioning

confidence: 99%

A Neuro-Evolutionary Approach to Electrocardiographic Signal Classification

Azzini

Dragoni

Tettamanzi

2014

Evolution, Complexity and Artificial Life

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Abstract. This chapter presents an evolutionary ANN classifier system as a heartbeat classification algorithm designed according to the rules of the PhysioNet/Computing in Cardiology Challenge 2011 [7], whose aim is to develop an efficient algorithm able to run within a mobile phone, that can provide useful feedback in the process of acquiring a diagnostically useful 12-lead Electrocardiography (ECG) recording. The method used to solve this problem is to apply a very powerful natural computing analysis tool, namely evolutionary neural networks, based on the joint evolution of the topology and the connection weights relying on a novel similarity-based crossover. The chapter focuses on discerning between usable and unusable electrocardiograms tele-medically acquired from mobile embedded devices. A preprocessing algorithm based on the Discrete Fourier Trasform has been applied before the evolutionary approach in order to extract the ECG feature dataset in the frequency domain. Finally, a series of tests has been carried out in order to evaluate the performance and the accuracy of the classifier system for such a challenge.

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“…Here we describe the main aspects of neuroevolution [46] (i.e. evolution of artificial neural networks).…”

Section: Selecting/generating Algorithm Components For Classificationmentioning

confidence: 99%

Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms

Pappa

Ochoa

Hyde

et al. 2013

Genet Program Evolvable Mach

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The fields of machine meta-learning and hyper-heuristic optimisation have developed mostly independently of each other, although evolutionary algorithms (particularly genetic programming) have recently played an important role in the development of both fields. Recent work in both fields shares a common goal, that of automating as much of the algorithm design process as possible. In this paper we first provide a historical perspective on automated algorithm design, and then we discuss similarities and differences between meta-learning in the field of supervised machine learning (classification) and hyper-heuristics in the field of optimisation. This discussion focuses on the dimensions of the problem space, the algorithm space and the performance measure, as well as clarifying important issues related to different levels of automation and generality in both fields. We also discuss important research directions, challenges and foundational issues in meta-learning and hyper-heuristic research. It is important to emphasize that this paper is not a survey, as several surveys on the areas of meta-learning and hyper-heuristics (separately) have been previously published. The main contribution of the paper is to contrast meta-learning and hyper-heuristics methods and concepts, in order to 123Genet Program Evolvable Mach DOI 10.1007/s10710-013-9186-9 promote awareness and cross-fertilisation of ideas across the (by and large, nonoverlapping) different communities of meta-learning and hyper-heuristic researchers. We hope that this cross-fertilisation of ideas can inspire interesting new research in both fields and in the new emerging research area which consists of integrating those fields.

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Neuroevolution: from architectures to learning

Cited by 598 publications

References 75 publications

From evolutionary computation to the evolution of things

From evolutionary computation to the evolution of things

A Neuro-Evolutionary Approach to Electrocardiographic Signal Classification

Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms

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