Publications associées à Neuroevolution

Combining biophysical models and machine learning to optimize implant geometry and stimulation protocol for intraneural electrodes

Silvestro Micera, Simone Romeni, Elena Losanno, Luca Pierantoni

Objective. Peripheral nerve interfaces have the potential to restore sensory, motor, and visceral functions. In particular, intraneural interfaces allow targeting deep neural structures with high selectivity, even if their performance strongly depends upon ...

IOP Publishing Ltd2023

Second-order guarantees in centralized, federated and decentralized nonconvex optimization

Ali H. Sayed, Stefan Vlaski

Rapid advances in data collection and processing capabilities have allowed for the use of increasingly complex models that give rise to nonconvex optimization problems. These formulations, however, can be arbitrarily difficult to solve in general, in the s ...

INT PRESS BOSTON, INC2020

Capsule networks, but not convolutional networks explain global configurational visual effects

Michael Herzog, Adrien Christophe Doerig, Alban Bornet

In human vision, perception of local features depends on all elements in the visual field and their exact configuration. For example, observers performed a vernier discrimination task. When a surrounding square was added to the vernier, the task became muc ...

2019

Error Feedback Fixes SignSGD and other Gradient Compression Schemes

Martin Jaggi, Sebastian Urban Stich, Quentin Rebjock, Sai Praneeth Reddy Karimireddy

Sign-based algorithms (e.g. signSGD) have been proposed as a biased gradient compression technique to alleviate the communication bottleneck in training large neural networks across multiple workers. We show simple convex counter-examples where signSGD doe ...

PMLR2019

IMPACT: Interval-based Multi-pass Proteomic Alignment with Constant Traceback

James Richard Larus, Sahand Kashani, Stuart Anthony Byma

Darwin is a genomics co-processor that achieved a 15000x acceleration on long read assembly through innovative hardware and algorithm co-design. Darwins algorithms and hardware implementation were specifically designed for DNA analysis pipelines. This pape ...

2019

Memetic Viability Evolution for Constrained Optimization

Dario Floreano, Andrea Maesani, Giovanni Iacca

The performance of evolutionary algorithms can be heavily undermined when constraints limit the feasible areas of the search space. For instance, while Covariance Matrix Adapta- tion Evolution Strategy is one of the most efficient algorithms for unconstrai ...

Institute of Electrical and Electronics Engineers2016

Redesigning Water Distribution Networks Using a Guided Evolutionary Approach

Anton Schleiss, José Pedro Gamito de Saldanha Calado Matos

The paper describes and discusses the application of a guided evolutionary approach for the optimization of water distribution networks. Initially developed for the Battle of the Water Networks II (BWN-II), the approach was adapted and improved for competi ...

American Society of Civil Engineers2016

Building shape optimisation to reduce air-conditioning needs using constrained evolutionary algorithms

Gianpiero Caruso

The purpose of this paper is to analyse the optimal three-dimensional form of buildings that minimise energy consumption due to solar irradiation. We use an evolutionary algorithm (hybrid CMA-ES/HDE algorithm) already applied to maximise solar energy utili ...

Pergamon-Elsevier Science Ltd2015

Viability evolutionary algorithms and applications to neuroscience and biology

Andrea Maesani

Evolutionary algorithms are heuristic methods that operate on a population of individuals, which represent candidate solutions to optimization problems. Individuals with better quality, or fitness, are reproduced at a higher rate than less fit individuals ...

EPFL2015

CMA-ES with Restarts for Solving CEC 2013 Benchmark Problems

Ilya Loshchilov

This paper investigates the performance of 6 versions of Covariance Matrix Adaptation Evolution Strategy (CMAES) with restarts on a set of 28 noiseless optimization problems (including 23 multi-modal ones) designed for the special session on real-parameter ...

2013

Kernel regression for real-time building energy analysis

Matthew Brown

This study proposes a new technique for real-time building energy modelling and event detection using kernel regression. We show that this technique can exceed the performance of conventional neural network algorithms, and do so by a large margin when the ...

Taylor & Francis Ltd2012

Hybrid Evolutionary-Neural Network Approach in Generation of Artificial Accelerograms Using Principal Component Analysis and Wavelet-Packet Transform

Mostafa Ajallooeian, Sanaz Saadat, Alireza Shahjouei

A novel hybrid evolutionary neural network method to generate multiple spectrum-compatible artificial earthquake accelerograms (SCAEAs) is presented. Genetic algorithm is employed to optimize the weight values of networks. In order to improve the training ...

Taylor & Francis2011

Optimisation of building form for solar energy utilisation using constrained evolutionary algorithms

Jérôme Henri Kämpf, Darren Robinson

In this paper we describe a new methodology for optimising building and urban geometric forms for the utilisation of solar irradiation, whether by passive or active means. For this we use a new evolutionary algorithm(a hybrid CMA-ES/HDE algorithm) to searc ...

Elsevier2010

Genetic representation and evolvability of modular neural controllers

Dario Floreano, Claudio Mattiussi, Peter Dürr

The manual design of con- trol systems for robotic devices can be challenging. Methods for the automatic synthesis of control systems, such as the evolution of artificial neural networks, are thus widely used in the robotics community. However, in many rob ...

Institute of Electrical and Electronics Engineers2010

A hybrid CMA-ES and HDE optimisation algorithm with application to solar energy potential

Jérôme Henri Kämpf, Darren Robinson

This paper describes the results of initial experiments to apply computational algorithms to explore a large parameter space containing many variables in the search for an optimal solution for the sustainable design of an urban development using a potentia ...

2009

Neuroevolution: from architectures to learning

Dario Floreano, Claudio Mattiussi, Peter Dürr

Artificial neural networks are applied to many real-world problems, ranging from pattern classification to robot control. In order to design a neural network for a particular task, the choice of an architecture (including the choice of a neuron model), and ...

2008

A Parallel Evolutionary Algorithm to Optimize Dynamic Data Types in Embedded Systems

David Atienza Alonso, José Luis Risco Martin

New multimedia embedded applications are increasingly dynamic, and rely on dynamically-allocated data types (DDTs) to store their data. The optimization of DDTs for each target embedded system is a time-consuming process due to the large searching space of ...

2008

Evolving Neural Controllers for Collective Robotic Inspection

Alcherio Martinoli

In this paper, an automatic synthesis methodology based on evolutionary computation is applied to evolve neural controllers for a homogeneous team of miniature autonomous mobile robots. Both feed-forward and recurrent neural networks can be evolved with fi ...

2006

Multi-cellular reconfigurable circuits : evolution, morphogenesis and learning

Daniel Roggen

Bio-inspired electronic circuits have the potential to address some of the shortcomings of conventional electronic circuits, such as lack of applicability to ill-defined problems, of robustness, or of adaptivity to unexpectedly changing environments. Bio-i ...

EPFL2005