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Journal articles on the topic 'NeuroEvolution of Augmenting Topologies'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 February 2022

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1

Stanley, Kenneth O., and Risto Miikkulainen. "Evolving Neural Networks through Augmenting Topologies." Evolutionary Computation 10, no. 2 (2002): 99–127. http://dx.doi.org/10.1162/106365602320169811.

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An important question in neuroevolution is how to gain an advantage from evolving neural network topologies along with weights. We present a method, NeuroEvolution of Augmenting Topologies (NEAT), which outperforms the best fixed-topology method on a challenging benchmark reinforcement learning task. We claim that the increased efficiency is due to (1) employing a principled method of crossover of different topologies, (2) protecting structural innovation using speciation, and (3) incrementally growing from minimal structure. We test this claim through a series of ablation studies that demonst

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2

Belhaj Slimene, Senda, and Chokri Mamoghli. "NeuroEvolution of Augmenting Topologies for predicting financial distress: A multicriteria decision analysis." Journal of Multi-Criteria Decision Analysis 26, no. 5-6 (2019): 320–28. http://dx.doi.org/10.1002/mcda.1669.

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Yuksel, Mehmet Erkan. "Agent-based evacuation modeling with multiple exits using NeuroEvolution of Augmenting Topologies." Advanced Engineering Informatics 35 (January 2018): 30–55. http://dx.doi.org/10.1016/j.aei.2017.11.003.

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Stanley, Kenneth O., David B. D'Ambrosio, and Jason Gauci. "A Hypercube-Based Encoding for Evolving Large-Scale Neural Networks." Artificial Life 15, no. 2 (2009): 185–212. http://dx.doi.org/10.1162/artl.2009.15.2.15202.

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Research in neuroevolution—that is, evolving artificial neural networks (ANNs) through evolutionary algorithms—is inspired by the evolution of biological brains, which can contain trillions of connections. Yet while neuroevolution has produced successful results, the scale of natural brains remains far beyond reach. This article presents a method called hypercube-based NeuroEvolution of Augmenting Topologies (HyperNEAT) that aims to narrow this gap. HyperNEAT employs an indirect encoding called connective compositional pattern-producing networks (CPPNs) that can produce connectivity patterns w

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Grisci, Bruno, and Márcio Dorn. "NEAT-FLEX: Predicting the conformational flexibility of amino acids using neuroevolution of augmenting topologies." Journal of Bioinformatics and Computational Biology 15, no. 03 (2017): 1750009. http://dx.doi.org/10.1142/s0219720017500093.

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The development of computational methods to accurately model three-dimensional protein structures from sequences of amino acid residues is becoming increasingly important to the structural biology field. This paper addresses the challenge of predicting the tertiary structure of a given amino acid sequence, which has been reported to belong to the NP-Complete class of problems. We present a new method, namely NEAT–FLEX, based on NeuroEvolution of Augmenting Topologies (NEAT) to extract structural features from (ABS) proteins that are determined experimentally. The proposed method manipulates st

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Menon, Unnikrishnan, and Anirudh Menon. "An Efficient Application of Neuroevolution for Competitive Multiagent Learning." Transactions on Machine Learning and Artificial Intelligence 9, no. 3 (2021): 1–13. http://dx.doi.org/10.14738/tmlai.93.10149.

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Multiagent systems provide an ideal environment for the evaluation and analysis of real-world problems using reinforcement learning algorithms. Most traditional approaches to multiagent learning are affected by long training periods as well as high computational complexity. NEAT (NeuroEvolution of Augmenting Topologies) is a popular evolutionary strategy used to obtain the best performing neural network architecture often used to tackle optimization problems in the field of artificial intelligence. This paper utilizes the NEAT algorithm to achieve competitive multiagent learning on a modified

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Jha, Sunil Kr, and Filip Josheski. "Artificial evolution using neuroevolution of augmenting topologies (NEAT) for kinetics study in diverse viscous mediums." Neural Computing and Applications 29, no. 12 (2016): 1337–47. http://dx.doi.org/10.1007/s00521-016-2664-2.

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Wang, Guochang, Guojian Cheng, and Timothy R. Carr. "The application of improved NeuroEvolution of Augmenting Topologies neural network in Marcellus Shale lithofacies prediction." Computers & Geosciences 54 (April 2013): 50–65. http://dx.doi.org/10.1016/j.cageo.2013.01.022.

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Secretan, Jimmy, Nicholas Beato, David B. D'Ambrosio, et al. "Picbreeder: A Case Study in Collaborative Evolutionary Exploration of Design Space." Evolutionary Computation 19, no. 3 (2011): 373–403. http://dx.doi.org/10.1162/evco_a_00030.

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For domains in which fitness is subjective or difficult to express formally, interactive evolutionary computation (IEC) is a natural choice. It is possible that a collaborative process combining feedback from multiple users can improve the quality and quantity of generated artifacts. Picbreeder, a large-scale online experiment in collaborative interactive evolution (CIE), explores this potential. Picbreeder is an online community in which users can evolve and share images, and most importantly, continue evolving others' images. Through this process of branching from other images, and through c

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10

Stanley, K. O., and R. Miikkulainen. "Competitive Coevolution through Evolutionary Complexification." Journal of Artificial Intelligence Research 21 (February 1, 2004): 63–100. http://dx.doi.org/10.1613/jair.1338.

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Two major goals in machine learning are the discovery and improvement of solutions to complex problems. In this paper, we argue that complexification, i.e. the incremental elaboration of solutions through adding new structure, achieves both these goals. We demonstrate the power of complexification through the NeuroEvolution of Augmenting Topologies (NEAT) method, which evolves increasingly complex neural network architectures. NEAT is applied to an open-ended coevolutionary robot duel domain where robot controllers compete head to head. Because the robot duel domain supports a wide range of st

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Gauci, Jason, and Kenneth O. Stanley. "Autonomous Evolution of Topographic Regularities in Artificial Neural Networks." Neural Computation 22, no. 7 (2010): 1860–98. http://dx.doi.org/10.1162/neco.2010.06-09-1042.

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Looking to nature as inspiration, for at least the past 25 years, researchers in the field of neuroevolution (NE) have developed evolutionary algorithms designed specifically to evolve artificial neural networks (ANNs). Yet the ANNs evolved through NE algorithms lack the distinctive characteristics of biological brains, perhaps explaining why NE is not yet a mainstream subject of neural computation. Motivated by this gap, this letter shows that when geometry is introduced to evolved ANNs through the hypercube-based neuroevolution of augmenting topologies algorithm, they begin to acquire charac

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Lang, Sebastian, Tobias Reggelin, Johann Schmidt, Marcel Müller, and Abdulrahman Nahhas. "NeuroEvolution of augmenting topologies for solving a two-stage hybrid flow shop scheduling problem: A comparison of different solution strategies." Expert Systems with Applications 172 (June 2021): 114666. http://dx.doi.org/10.1016/j.eswa.2021.114666.

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13

Kang Kim, Hojin, Raimundo Becerra, Sandy Bolufé, Cesar A. Azurdia-Meza, Samuel Montejo-Sánchez, and David Zabala-Blanco. "Neuroevolution-Based Adaptive Antenna Array Beamforming Scheme to Improve the V2V Communication Performance at Intersections." Sensors 21, no. 9 (2021): 2956. http://dx.doi.org/10.3390/s21092956.

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The opportunistic exchange of information between vehicles can significantly contribute to reducing the occurrence of accidents and mitigating their damages. However, in urban environments, especially at intersection scenarios, obstacles such as buildings and walls block the line of sight between the transmitter and receiver, reducing the vehicular communication range and thus harming the performance of road safety applications. Furthermore, the sizes of the surrounding vehicles and weather conditions may affect the communication. This makes communications in urban V2V communication scenarios

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14

Risi, Sebastian, and Kenneth O. Stanley. "An Enhanced Hypercube-Based Encoding for Evolving the Placement, Density, and Connectivity of Neurons." Artificial Life 18, no. 4 (2012): 331–63. http://dx.doi.org/10.1162/artl_a_00071.

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Intelligence in nature is the product of living brains, which are themselves the product of natural evolution. Although researchers in the field of neuroevolution (NE) attempt to recapitulate this process, artificial neural networks (ANNs) so far evolved through NE algorithms do not match the distinctive capabilities of biological brains. The recently introduced hypercube-based neuroevolution of augmenting topologies (HyperNEAT) approach narrowed this gap by demonstrating that the pattern of weights across the connectivity of an ANN can be generated as a function of its geometry, thereby allow

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Tan, Maxine, Jiantao Pu, and Bin Zheng. "Optimization of Network Topology in Computer-Aided Detection Schemes Using Phased Searching with NEAT in a Time-Scaled Framework." Cancer Informatics 13s1 (January 2014): CIN.S13885. http://dx.doi.org/10.4137/cin.s13885.

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In the field of computer-aided mammographic mass detection, many different features and classifiers have been tested. Frequently, the relevant features and optimal topology for the artificial neural network (ANN)-based approaches at the classification stage are unknown, and thus determined by trial-and-error experiments. In this study, we analyzed a classifier that evolves ANNs using genetic algorithms (GAs), which combines feature selection with the learning task. The classifier named “Phased Searching with NEAT in a Time-Scaled Framework” was analyzed using a dataset with 800 malignant and 8

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Hamann, Heiko, Thomas Schmickl, and Karl Crailsheim. "A Hormone-Based Controller for Evaluation-Minimal Evolution in Decentrally Controlled Systems." Artificial Life 18, no. 2 (2012): 165–98. http://dx.doi.org/10.1162/artl_a_00058.

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One of the main challenges in automatic controller synthesis is to develop methods that can successfully be applied for complex tasks. The difficulty is increased even more in the case of settings with multiple interacting agents. We apply the artificial homeostatic hormone system (AHHS) approach, which is inspired by the signaling network of unicellular organisms, to control a system of several independently acting agents decentrally. The approach is designed for evaluation-minimal, artificial evolution in order to be applicable to complex modular robotics scenarios. The performance of AHHS c

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Hartl, Benedikt, Maximilian Hübl, Gerhard Kahl, and Andreas Zöttl. "Microswimmers learning chemotaxis with genetic algorithms." Proceedings of the National Academy of Sciences 118, no. 19 (2021): e2019683118. http://dx.doi.org/10.1073/pnas.2019683118.

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Various microorganisms and some mammalian cells are able to swim in viscous fluids by performing nonreciprocal body deformations, such as rotating attached flagella or by distorting their entire body. In order to perform chemotaxis (i.e., to move toward and to stay at high concentrations of nutrients), they adapt their swimming gaits in a nontrivial manner. Here, we propose a computational model, which features autonomous shape adaptation of microswimmers moving in one dimension toward high field concentrations. As an internal decision-making machinery, we use artificial neural networks, which

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18

Kim, Eric J., and Ruben E. Perez. "Neuroevolutionary Control for Autonomous Soaring." Aerospace 8, no. 9 (2021): 267. http://dx.doi.org/10.3390/aerospace8090267.

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The energy efficiency and flight endurance of small unmanned aerial vehicles (SUAVs) can be improved through the implementation of autonomous soaring strategies. Biologically inspired flight techniques such as dynamic and thermal soaring offer significant energy savings through the exploitation of naturally occurring wind phenomena for thrustless flight. Recent interest in the application of artificial intelligence algorithms for autonomous soaring has been motivated by the pursuit of instilling generalized behavior in control systems, centered around the use of neural networks. However, the t

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19

Stanovov, V., Sh Akhmedova, and E. Semenkin. "Neuroevolution of augmented topologies with difference-based mutation." IOP Conference Series: Materials Science and Engineering 1047, no. 1 (2021): 012075. http://dx.doi.org/10.1088/1757-899x/1047/1/012075.

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20

Baldominos, Alejandro, Yago Saez, and Pedro Isasi. "Hybridizing Evolutionary Computation and Deep Neural Networks: An Approach to Handwriting Recognition Using Committees and Transfer Learning." Complexity 2019 (March 26, 2019): 1–16. http://dx.doi.org/10.1155/2019/2952304.

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Neuroevolution is the field of study that uses evolutionary computation in order to optimize certain aspect of the design of neural networks, most often its topology and hyperparameters. The field was introduced in the late-1980s, but only in the latest years the field has become mature enough to enable the optimization of deep learning models, such as convolutional neural networks. In this paper, we rely on previous work to apply neuroevolution in order to optimize the topology of deep neural networks that can be used to solve the problem of handwritten character recognition. Moreover, we tak

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21

Stanovov, Vladimir, Shakhnaz Akhmedova, and Eugene Semenkin. "Difference-Based Mutation Operation for Neuroevolution of Augmented Topologies." Algorithms 14, no. 5 (2021): 127. http://dx.doi.org/10.3390/a14050127.

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In this paper, a novel search operation is proposed for the neuroevolution of augmented topologies, namely the difference-based mutation. This operator uses the differences between individuals in the population to perform more efficient search for optimal weights and structure of the model. The difference is determined according to the innovation numbers assigned to each node and connection, allowing tracking the changes. The implemented neuroevolution algorithm allows backward connections and loops in the topology, and uses a set of mutation operators, including connections merging and deleti

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22

Cussat-Blanc, Sylvain, Kyle Harrington, and Jordan Pollack. "Gene Regulatory Network Evolution Through Augmenting Topologies." IEEE Transactions on Evolutionary Computation 19, no. 6 (2015): 823–37. http://dx.doi.org/10.1109/tevc.2015.2396199.

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23

NISHIHARA, Shino, Shinichi TAKATA, and Kazuhiro OHKURA. "2A1-E04 System Tuning of NeuroEvolution of Augmented Topologies for an autonomous robot." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2006 (2006): _2A1—E04_1—_2A1—E04_4. http://dx.doi.org/10.1299/jsmermd.2006._2a1-e04_1.

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24

Doroshenko, A. Y., and I. Z. Achour. "Application of neuro evolution tools in automation of technical control systems." PROBLEMS IN PROGRAMMING, no. 1 (March 2021): 016–25. http://dx.doi.org/10.15407/pp2021.01.016.

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Reinforced learning is a field of machine learning based on how software agents should perform actions in the environment to maximize the concept of cumulative reward. This paper proposes a new application of machine reinforcement learning techniques in the form of neuro-evolution of augmenting topologies to solve control automation problems using modeling control problems of technical systems. Key application components include OpenAI Gym toolkit for develop-ing and comparing reinforcement learn-ing algorithms, full-fledged open-source implementation of the NEAT genetic al-gorithm called Shar

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25

Babu, M. Suresh, and E. Keshava Reddy. "Investigations on Improvised Neural Network Chess Engine for Augmenting Topologies." International Journal of Mathematics Trends and Technology 48, no. 3 (2017): 214–17. http://dx.doi.org/10.14445/22315373/ijmtt-v48p530.

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26

Leoshchenko, S. D., S. A. Subbotin, A. O. Oliinyk, and O. E. Narivs’kiy. "IMPLEMENTATION OF THE INDICATOR SYSTEM IN MODELING OF COMPLEX TECHNICAL SYSTEMS." Radio Electronics, Computer Science, Control 1, no. 1 (2021): 117–26. http://dx.doi.org/10.15588/1607-3274-2021-1-12.

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Context. The problem of determining the optimal topology of a neuromodel, which is characterized by a high level of logical transparency in modeling complex technical systems, is considered. The object of research is the process of applying an indicator system to simplify and select the topology of neuromodels. Objective of the work is to develop and use a system of indicators to determine the level of complexity of the modeling problem and gradually select the optimal logically transparent topology of the neuromodel. Method. A method is proposed for selecting an optimal, logically

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27

Papavasileiou, Evgenia, Jan Cornelis, and Bart Jansen. "A Systematic Literature Review of the Successors of ‘NeuroEvolution of Augmenting Topologies’." Evolutionary Computation, November 5, 2020, 1–73. http://dx.doi.org/10.1162/evco_a_00282.

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NeuroEvolution (NE) refers to a family of methods for optimizing Artificial Neural Networks (ANNs) using Evolutionary Computation (EC) algorithms. NeuroEvolution of Augmenting Topologies (NEAT) is considered one of the most influential algorithms in the field. Eighteen years after its invention, a plethora of methods have been proposed that extend NEAT in different aspects. In this paper we present a systematic literature review (SLR) to list and categorize the methods succeeding NEAT. Our review protocol identified 232 papers by merging the findings of two major electronic databases. Applying

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28

Binoti, Daniel Henrique Breda, Paulo Junio Duarte, Mayra Luiza Marques da Silva, et al. "ESTIMATION OF HEIGHT OF EUCALYPTUS TREES WITH NEUROEVOLUTION OF AUGMENTING TOPOLOGIES (NEAT)." Revista Árvore 41, no. 3 (2018). http://dx.doi.org/10.1590/1806-90882017000300014.

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ABSTRACT The aim of this study was to evaluate the method of neuroevolution of augmenting topologies (NEAT) to adjust the weights and the topology of artificial neural networks (ANNs) in the estimation of tree height in a clonal population of eucalyptus, and compare with estimates obtained by a hypsometric regression model. To estimate the total tree height (Ht), the RNAs and the regression model, we used as variables a diameter of 1.3 m height (dbh) and the dominant height (Hd). The RNAs were adjusted and applied to the computer system NeuroForest, varying the size of the initial population (

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29

Wijaya, A. B. M., D. S. Ikawahyuni, Rospita Gea, and Febe Maedjaja. "Role Comparison between Deep Belief Neural Network and NeuroEvolution of Augmenting Topologies to Detect Diabetes." JOIV : International Journal on Informatics Visualization 5, no. 2 (2021). http://dx.doi.org/10.30630/joiv.5.2.448.

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Diabetes in Indonesia has been perceived as a grave health problem and has been a concern since the early 1980’s [2]. The prevalence of diabetes in adults in Indonesia, as stated by IDF, was 6.2% with the total case amounting to 10.681.400. Moreover, Indonesia is also in the top ten global countries with the highest diabetes case in 2013. This research will investigate the role of Deep Belief Network (DBN) and NeuroEvolution of Augmenting Topology (NEAT) in solving regression problems in detecting diabetes. DBN works by processing the data in unsupervised network architectures. The algorithm p

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30

Seriani, Stefano, Luca Marcini, Matteo Caruso, Paolo Gallina, and Eric Medvet. "Crowded Environment Navigation with NEAT: Impact of Perception Resolution on Controller Optimization." Journal of Intelligent & Robotic Systems 101, no. 2 (2021). http://dx.doi.org/10.1007/s10846-020-01308-8.

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AbstractCrowd navigation with autonomous systems is a topic which has seen a rapid increase in interest recently. While it appears natural to humans, being able to reach a target can prove difficult or impossible to a mobile robot because of the safety issues related to collisions with people. In this work we propose an approach to control a robot in a crowded environment; the method employs an Artificial Neural Network (ANN) that is trained with the NeuroEvolution of Augmented Topologies (NEAT) method. Models for the kinematics, perception, and cognition of the robot are presented. In particu

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31

Arnold, Bruce, and Margalit Levin. "Ambient Anomie in the Virtualised Landscape? Autonomy, Surveillance and Flows in the 2020 Streetscape." M/C Journal 13, no. 2 (2010). http://dx.doi.org/10.5204/mcj.221.

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Our thesis is that the city’s ambience is now an unstable dialectic in which we are watchers and watched, mirrored and refracted in a landscape of iPhone auteurs, eTags, CCTV and sousveillance. Embrace ambience! Invoking Benjamin’s spirit, this article does not seek to limit understanding through restriction to a particular theme or theoretical construct (Buck-Morss 253). Instead, it offers snapshots of interactions at the dawn of the postmodern city. That bricolage also engages how people appropriate, manipulate, disrupt and divert urban spaces and strategies of power in their everyday life.

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