Using Genetic Algorithms to Evolve Artificial Neural Networks
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Volume 3 Summer 2012
Volume 3 Summer 2012 . Academic Partners . Cover image Magnetic resonance image of the human brain showing colour-coded regions activated by smell stimulus. Editors Ulisses Barres de Almeida Max-Planck-Institut fuer Physik [email protected] Juan Rojo TH Unit, PH Division, CERN [email protected] [email protected] Academic Partners Fondazione CEUR Consortium Nova Universitas Copyright ©2012 by Associazione EURESIS The user may not modify, copy, reproduce, retransmit or otherwise distribute this publication and its contents (whether text, graphics or original research content), without express permission in writing from the Editors. Where the above content is directly or indirectly reproduced in an academic context, this must be acknowledge with the appropriate bibliographical citation. The opinions stated in the papers of the Euresis Journal are those of their respective authors and do not necessarily reflect the opinions of the Editors or the members of the Euresis Association or its sponsors. Euresis Journal (ISSN 2239-2742), a publication of Associazione Euresis, an Association for the Promotion of Scientific Endevour, Via Caduti di Marcinelle 2, 20134 Milano, Italia. www.euresisjournal.org Contact information: Email. [email protected] Tel.+39-022-1085-2225 Fax. +39-022-1085-2222 Graphic design and layout Lorenzo Morabito Technical Editor Davide PJ Caironi This document was created using LATEX 2" and X LE ATEX 2 . Letter from the Editors Dear reader, with this new issue we reach the third volume of Euresis Journal, an editorial ad- venture started one year ago with the scope of opening up a novel space of debate and encounter within the scientific and academic communities. -
Optimization of Deep Architectures for EEG Signal Classification
sensors Article Optimization of Deep Architectures for EEG Signal Classification: An AutoML Approach Using Evolutionary Algorithms Diego Aquino-Brítez 1, Andrés Ortiz 2,* , Julio Ortega 1, Javier León 1, Marco Formoso 2 , John Q. Gan 3 and Juan José Escobar 1 1 Department of Computer Architecture and Technology, University of Granada, 18014 Granada, Spain; [email protected] (D.A.-B.); [email protected] (J.O.); [email protected] (J.L.); [email protected] (J.J.E.) 2 Department of Communications Engineering, University of Málaga, 29071 Málaga, Spain; [email protected] 3 School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK; [email protected] * Correspondence: [email protected] Abstract: Electroencephalography (EEG) signal classification is a challenging task due to the low signal-to-noise ratio and the usual presence of artifacts from different sources. Different classification techniques, which are usually based on a predefined set of features extracted from the EEG band power distribution profile, have been previously proposed. However, the classification of EEG still remains a challenge, depending on the experimental conditions and the responses to be captured. In this context, the use of deep neural networks offers new opportunities to improve the classification performance without the use of a predefined set of features. Nevertheless, Deep Learning architec- Citation: Aquino-Brítez, D.; Ortiz, tures include a vast number of hyperparameters on which the performance of the model relies. In this A.; Ortega, J.; León, J.; Formoso, M.; paper, we propose a method for optimizing Deep Learning models, not only the hyperparameters, Gan, J.Q.; Escobar, J.J. -
Covariance Matrix Adaptation for the Rapid Illumination of Behavior Space
Covariance Matrix Adaptation for the Rapid Illumination of Behavior Space Matthew C. Fontaine Julian Togelius Viterbi School of Engineering Tandon School of Engineering University of Southern California New York University Los Angeles, CA New York City, NY [email protected] [email protected] Stefanos Nikolaidis Amy K. Hoover Viterbi School of Engineering Ying Wu College of Computing University of Southern California New Jersey Institute of Technology Los Angeles, CA Newark, NJ [email protected] [email protected] ABSTRACT We focus on the challenge of finding a diverse collection of quality solutions on complex continuous domains. While quality diver- sity (QD) algorithms like Novelty Search with Local Competition (NSLC) and MAP-Elites are designed to generate a diverse range of solutions, these algorithms require a large number of evaluations for exploration of continuous spaces. Meanwhile, variants of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) are among the best-performing derivative-free optimizers in single- objective continuous domains. This paper proposes a new QD algo- rithm called Covariance Matrix Adaptation MAP-Elites (CMA-ME). Figure 1: Comparing Hearthstone Archives. Sample archives Our new algorithm combines the self-adaptation techniques of for both MAP-Elites and CMA-ME from the Hearthstone ex- CMA-ES with archiving and mapping techniques for maintaining periment. Our new method, CMA-ME, both fills more cells diversity in QD. Results from experiments based on standard con- in behavior space and finds higher quality policies to play tinuous optimization benchmarks show that CMA-ME finds better- Hearthstone than MAP-Elites. Each grid cell is an elite (high quality solutions than MAP-Elites; similarly, results on the strategic performing policy) and the intensity value represent the game Hearthstone show that CMA-ME finds both a higher overall win rate across 200 games against difficult opponents. -
A Neuroevolution Approach to Imitating Human-Like Play in Ms
A Neuroevolution Approach to Imitating Human-Like Play in Ms. Pac-Man Video Game Maximiliano Miranda, Antonio A. S´anchez-Ruiz, and Federico Peinado Departamento de Ingenier´ıadel Software e Inteligencia Artificial Universidad Complutense de Madrid c/ Profesor Jos´eGarc´ıaSantesmases 9, 28040 Madrid (Spain) [email protected] - [email protected] - [email protected] http://www.narratech.com Abstract. Simulating human behaviour when playing computer games has been recently proposed as a challenge for researchers on Artificial Intelligence. As part of our exploration of approaches that perform well both in terms of the instrumental similarity measure and the phenomeno- logical evaluation by human spectators, we are developing virtual players using Neuroevolution. This is a form of Machine Learning that employs Evolutionary Algorithms to train Artificial Neural Networks by consider- ing the weights of these networks as chromosomes for the application of genetic algorithms. Results strongly depend on the fitness function that is used, which tries to characterise the human-likeness of a machine- controlled player. Designing this function is complex and it must be implemented for specific games. In this work we use the classic game Ms. Pac-Man as an scenario for comparing two different methodologies. The first one uses raw data extracted directly from human traces, i.e. the set of movements executed by a real player and their corresponding time stamps. The second methodology adds more elaborated game-level parameters as the final score, the average distance to the closest ghost, and the number of changes in the player's route. We assess the impor- tance of these features for imitating human-like play, aiming to obtain findings that would be useful for many other games. -
Neuro-Crítica: Un Aporte Al Estudio De La Etiología, Fenomenología Y Ética Del Uso Y Abuso Del Prefijo Neuro1
JAHR ǀ Vol. 4 ǀ No. 7 ǀ 2013 Professional article Amir Muzur, Iva Rincic Neuro-crítica: un aporte al estudio de la etiología, fenomenología y ética del uso y abuso del prefijo neuro1 ABSTRACT The last few decades, beside being proclaimed "the decades of the brain" or "the decades of the mind," have witnessed a fascinating explosion of new disciplines and pseudo-disciplines characterized by the prefix neuro-. To the "old" specializations of neurosurgery, neurophysiology, neuropharmacology, neurobiology, etc., some new ones have to be added, which might sound somehow awkward, like neurophilosophy, neuroethics, neuropolitics, neurotheology, neuroanthropology, neuroeconomy, and other. Placing that phenomenon of "neuroization" of all fields of human thought and practice into a context of mostly unjustified and certainly too high – almost millenarianistic – expectations of the science of the brain and mind at the end of the 20th century, the present paper tries to analyze when the use of the prefix neuro- is adequate and when it is dubious. Key words: brain, neuroscience, word coinage RESUMEN Las últimas décadas, además de haber sido declaradas "las décadas del cerebro" o "las décadas de la mente", han sido testigos de una fascinante explosión de nuevas disciplinas y pseudo-disciplinas caracterizadas por el prefijo neuro-. A las "antiguas" especializaciones de la neurocirugía, la neurofisiología, la neurofarmacología, la neurobiología, etc., deben agregarse otras nuevas que pueden sonar algo extrañas, como la neurofilosofía, la neuroética, la neuropolítica, la neuroteología, la neuroantropología, la neuroeconomía, entre otras. El presente trabajo coloca ese fenómeno de "neuroización" de todos los campos del pensamiento y la práctica humana en un contexto de expectativas en general injustificadas y sin duda altísimas (y casi milenarias) en la ciencia del cerebro y la mente a fines del siglo XX; e intenta analizar cuándo el uso del prefijo neuro- es adecuado y cuándo es cuestionable. -
Evolving Neural Networks Using Behavioural Genetic Principles
Evolving Neural Networks Using Behavioural Genetic Principles Maitrei Kohli A dissertation submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy Department of Computer Science & Information Systems Birkbeck, University of London United Kingdom March 2017 0 Declaration This thesis is the result of my own work, except where explicitly acknowledged in the text. Maitrei Kohli …………………. 1 ABSTRACT Neuroevolution is a nature-inspired approach for creating artificial intelligence. Its main objective is to evolve artificial neural networks (ANNs) that are capable of exhibiting intelligent behaviours. It is a widely researched field with numerous successful methods and applications. However, despite its success, there are still open research questions and notable limitations. These include the challenge of scaling neuroevolution to evolve cognitive behaviours, evolving ANNs capable of adapting online and learning from previously acquired knowledge, as well as understanding and synthesising the evolutionary pressures that lead to high-level intelligence. This thesis presents a new perspective on the evolution of ANNs that exhibit intelligent behaviours. The novel neuroevolutionary approach presented in this thesis is based on the principles of behavioural genetics (BG). It evolves ANNs’ ‘general ability to learn’, combining evolution and ontogenetic adaptation within a single framework. The ‘general ability to learn’ was modelled by the interaction of artificial genes, encoding the intrinsic properties of the ANNs, and the environment, captured by a combination of filtered training datasets and stochastic initialisation weights of the ANNs. Genes shape and constrain learning whereas the environment provides the learning bias; together, they provide the ability of the ANN to acquire a particular task. -
Evolving Autonomous Agent Controllers As Analytical Mathematical Models
Evolving Autonomous Agent Controllers as Analytical Mathematical Models Paul Grouchy1 and Gabriele M.T. D’Eleuterio1 1University of Toronto Institute for Aerospace Studies, Toronto, Ontario, Canada M3H 5T6 [email protected] Downloaded from http://direct.mit.edu/isal/proceedings-pdf/alife2014/26/681/1901537/978-0-262-32621-6-ch108.pdf by guest on 27 September 2021 Abstract a significant amount of time and computational resources. Furthermore, any such design decisions introduce additional A novel Artificial Life paradigm is proposed where experimenter bias into the simulation. Finally, complex be- autonomous agents are controlled via genetically- haviors require complex representations, adding to the al- encoded Evolvable Mathematical Models (EMMs). Agent/environment inputs are mapped to agent outputs ready high computational costs of ALife algorithms while via equation trees which are evolved using Genetic Pro- further obfuscating the inner workings of the evolved agents. gramming. Equations use only the four basic mathematical We propose Evolvable Mathematical Models (EMMs), a operators: addition, subtraction, multiplication and division. novel paradigm whereby autonomous agents are evolved via Experiments on the discrete Double-T Maze with Homing GP as analytical mathematical models of behavior. Agent problem are performed; the source code has been made available. Results demonstrate that autonomous controllers inputs are mapped to outputs via a system of genetically with learning capabilities can be evolved as analytical math- encoded equations. Only the four basic mathematical op- ematical models of behavior, and that neuroplasticity and erations (addition, subtraction, multiplication and division) neuromodulation can emerge within this paradigm without are required, as they can be used to approximate any ana- having these special functionalities specified a priori. -
Improving Fitness Functions in Genetic Programming for Classification on Unbalanced Credit Card Datasets
Improving Fitness Functions in Genetic Programming for Classification on Unbalanced Credit Card Datasets Van Loi Cao, Nhien An Le Khac, Miguel Nicolau, Michael O’Neill, James McDermott University College Dublin Abstract. Credit card fraud detection based on machine learning has recently attracted considerable interest from the research community. One of the most important tasks in this area is the ability of classifiers to handle the imbalance in credit card data. In this scenario, classifiers tend to yield poor accuracy on the fraud class (minority class) despite realizing high overall accuracy. This is due to the influence of the major- ity class on traditional training criteria. In this paper, we aim to apply genetic programming to address this issue by adapting existing fitness functions. We examine two fitness functions from previous studies and develop two new fitness functions to evolve GP classifier with superior accuracy on the minority class and overall. Two UCI credit card datasets are used to evaluate the effectiveness of the proposed fitness functions. The results demonstrate that the proposed fitness functions augment GP classifiers, encouraging fitter solutions on both the minority and the majority classes. 1 Introduction Credit cards have emerged as the preferred means of payment in response to continuous economic growth and rapid developments in information technology. The daily volume of credit card transactions reveals the shift from cash to card. Concurrently, the incidence of credit card fraud has risen with the loss of billions of dollars globally each year. Moreover, criminals have evolved increasingly more sophisticated strategies to exploit weaknesses in the security protocols. -
Automatic Synthesis of Working Memory Neural Networks with Neuroevolution Methods Tony Pinville, Stéphane Doncieux
Automatic synthesis of working memory neural networks with neuroevolution methods Tony Pinville, Stéphane Doncieux To cite this version: Tony Pinville, Stéphane Doncieux. Automatic synthesis of working memory neural networks with neu- roevolution methods. Cinquième conférence plénière française de Neurosciences Computationnelles, ”Neurocomp’10”, Aug 2010, Lyon, France. hal-00553407 HAL Id: hal-00553407 https://hal.archives-ouvertes.fr/hal-00553407 Submitted on 10 Mar 2011 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. AUTOMATIC SYNTHESIS OF WORKING MEMORY NEURAL NETWORKS WITH NEUROEVOLUTION METHODS Tony Pinville Stephane´ Doncieux ISIR, CNRS UMR 7222 ISIR, CNRS UMR 7222 Universite´ Pierre et Marie Curie-Paris 6 Universite´ Pierre et Marie Curie-Paris 6 4 place Jussieu, F-75252 4 place Jussieu, F-75252 Paris Cedex 05, France Paris Cedex 05, France email: [email protected] email: [email protected] ABSTRACT used in particular in Evolutionary Robotics to make real or simulated robots exhibit a desired behavior [3]. Evolutionary Robotics is a research field focused on Most evolutionary algorithms optimize a fixed size autonomous design of robots based on evolutionary algo- genotype, whereas neuroevolution methods aim at explor- rithms. -
Accelerating Evolutionary Algorithms with Gaussian Process Fitness Function Models Dirk Buche,¨ Nicol N
IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS — PART C: APPLICATIONS AND REVIEWS, VOL. 35, NO. 2, MAY 2005 183 Accelerating Evolutionary Algorithms with Gaussian Process Fitness Function Models Dirk Buche,¨ Nicol N. Schraudolph, and Petros Koumoutsakos Abstract— We present an overview of evolutionary algorithms II. MODELS IN EVOLUTIONARY ALGORITHMS that use empirical models of the fitness function to accelerate convergence, distinguishing between evolution control and the There are two main ways to integrate models into an surrogate approach. We describe the Gaussian process model evolutionary optimization. In the first, a fraction of individuals and propose using it as an inexpensive fitness function surrogate. is evaluated on the fitness function itself, the remainder Implementation issues such as efficient and numerically stable merely on its model. Jin et al. [7] refer to individuals that computation, exploration versus exploitation, local modeling, are evaluated on the fitness function as controlled, and call multiple objectives and constraints, and failed evaluations are ad- dressed. Our resulting Gaussian process optimization procedure this technique evolution control. In the second approach, the clearly outperforms other evolutionary strategies on standard test optimum of the model is determined, then evaluated on the functions as well as on a real-world problem: the optimization fitness function. The new evaluation is used to update the of stationary gas turbine compressor profiles. model, and the process is repeated with the improved model. Index Terms— evolutionary algorithms, fitness function mod- This surrogate approach evaluates only predicted optima on eling, evolution control, surrogate approach, Gaussian process, the fitness function, otherwise using the model as a surrogate. -
Neuroevolution
Neuroevolution Risto Miikkulainen The University of Texas at Austin 1 Definition Neuroevolution is a method for modifying neural network weights, topologies, or ensembles in order to learn a specific task. Evolutionary computation is used to search for network parameters that maximize a fitness function that measures performance in the task. Compared to other neural network learning methods, neuroevolution is highly general, allowing learning without explicit targets, with nondifferentiable activation functions, and with recurrent networks. It can also be combined with standard neural network learning to e.g. model biological adaptation. Neuroevolution can also be seen as a policy search method for reinforcement- learning problems, where it is well suited to continuous domains and to domains where the state is only partially observable. 2 Synonyms Evolving neural networks, genetic neural networks 3 Motivation and Background The primary motivation for neuroevolution is to be able to train neural networks in sequential decision tasks with sparse reinforcement information. Most neural network learning is concerned with supervised tasks, where the desired behavior is described in terms of a corpus of input-output examples. However, many learning tasks in the real world do not lend themselves to the supervised learning approach. For example, in game playing, vehicle control, and robotics, the optimal actions at each point in time are not always known; only after performing several actions it is possible to get information about how well they worked, such as winning or losing the game. Neuroevolution makes it possible to find a neural network that optimizes behavior given only such sparse information about how well the networks are doing, without direct information about what exactly they should be doing. -
Neuroevolution Strategies for Episodic Reinforcement Learning ∗ Verena Heidrich-Meisner , Christian Igel
J. Algorithms 64 (2009) 152–168 Contents lists available at ScienceDirect Journal of Algorithms Cognition, Informatics and Logic www.elsevier.com/locate/jalgor Neuroevolution strategies for episodic reinforcement learning ∗ Verena Heidrich-Meisner , Christian Igel Institut für Neuroinformatik, Ruhr-Universität Bochum, 44780 Bochum, Germany article info abstract Article history: Because of their convincing performance, there is a growing interest in using evolutionary Received 30 April 2009 algorithms for reinforcement learning. We propose learning of neural network policies Available online 8 May 2009 by the covariance matrix adaptation evolution strategy (CMA-ES), a randomized variable- metric search algorithm for continuous optimization. We argue that this approach, Keywords: which we refer to as CMA Neuroevolution Strategy (CMA-NeuroES), is ideally suited for Reinforcement learning Evolution strategy reinforcement learning, in particular because it is based on ranking policies (and therefore Covariance matrix adaptation robust against noise), efficiently detects correlations between parameters, and infers a Partially observable Markov decision process search direction from scalar reinforcement signals. We evaluate the CMA-NeuroES on Direct policy search five different (Markovian and non-Markovian) variants of the common pole balancing problem. The results are compared to those described in a recent study covering several RL algorithms, and the CMA-NeuroES shows the overall best performance. © 2009 Elsevier Inc. All rights reserved. 1. Introduction Neuroevolution denotes the application of evolutionary algorithms to the design and learning of neural networks [54,11]. Accordingly, the term Neuroevolution Strategies (NeuroESs) refers to evolution strategies applied to neural networks. Evolution strategies are a major branch of evolutionary algorithms [35,40,8,2,7].