Relevant bibliographies by topics / NeuroEvolution of Augmenting Topologies

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'NeuroEvolution of Augmenting Topologies'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 February 2022

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Journal articles on the topic "NeuroEvolution of Augmenting Topologies"

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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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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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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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Dissertations / Theses on the topic "NeuroEvolution of Augmenting Topologies"

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Sohangir, Soroosh. "Optimized feature selection using NeuroEvolution of Augmenting Topologies (NEAT)." OpenSIUC, 2011. https://opensiuc.lib.siu.edu/theses/767.

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AN ABSTRACT OF THE THESIS OF SOROOSH SOHANGIR, for the MASTER OF SCIENCE degree in COMPUTER SCIENCE, presented on 9 th November 2011, at Southern Illinois University Carbondale. TITLE: OPTIMIZED FEATURE SELECTION USING NEUROEVOLUTION OF AUGMENTING TOPOLOGIES (NEAT) MAJOR PROFESSOR: Dr. Shahram Rahimi Feature selection using the NeuroEvolution of Augmenting Topologies (NEAT) is a new approach. In this thesis an investigation had been carried out for implementation based on optimization of the network topology and protecting innovation through the speciation which is similar
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Woolley, Brian G. "Novelty-Assisted Interactive Evolution of Control Behaviors." Doctoral diss., University of Central Florida, 2012. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/5579.

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The field of evolutionary computation is inspired by the achievements of natural evolution, in which there is no final objective. Yet the pursuit of objectives is ubiquitous in simulated evolution because evolutionary algorithms that can consistently achieve established benchmarks are lauded as successful, thus reinforcing this paradigm. A significant problem is that such objective approaches assume that intermediate stepping stones will increasingly resemble the final objective when in fact they often do not. The consequence is that while solutions may exist, searching for such objectives m
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Lehman, Joel. "Evolution Through the Search for Novelty." Doctoral diss., University of Central Florida, 2012. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/5394.

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I present a new approach to evolutionary search called novelty search, wherein only behavioral novelty is rewarded, thereby abstracting evolution as a search for novel forms. This new approach contrasts with the traditional approach of rewarding progress towards the objective through an objective function. Although they are designed to light a path to the objective, objective functions can instead deceive search into converging to dead ends called local optima. As a significant problem in evolutionary computation, deception has inspired many techniques designed to mitigate it. However, near
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Hoover, Amy K. "Functional Scaffolding for Musical Composition: A New Approach in Computer-Assisted Music Composition." Doctoral diss., University of Central Florida, 2014. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/6290.

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While it is important for systems intended to enhance musical creativity to define and explore musical ideas conceived by individual users, many limit musical freedom by focusing on maintaining musical structure, thereby impeding the user's freedom to explore his or her individual style. This dissertation presents a comprehensive body of work that introduces a new musical representation that allows users to explore a space of musical rules that are created from their own melodies. This representation, called functional scaffolding for musical composition (FSMC), exploits a simple yet powerful
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Hoover, Amy K. "Neat drummer : computer-generated drum tracks." Honors in the Major Thesis, University of Central Florida, 2008. http://digital.library.ucf.edu/cdm/ref/collection/ETH/id/1089.

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This item is only available in print in the UCF Libraries. If this is your Honors Thesis, you can help us make it available online for use by researchers around the world by following the instructions on the distribution consent form at http://library.ucf.edu/Systems/DigitalInitiatives/DigitalCollections/InternetDistributionConsentAgreementForm.pdf You may also contact the project coordinator, Kerri Bottorff, at kerri.bottorff@ucf.edu for more information.<br>Bachelors<br>Engineering and Computer Science<br>Computer Science
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Gustafsson, Jimmy. "Evolving Neuromodulatory Topologies for Plasticity in Video Game Playing." Thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-12888.

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In the last decades neural networks have become more frequent in video games. Neuroevolution helps us generate optimal network topologies for specific tasks, but there are still still unexplored areas of neuroevolution, and ways of improving the performance of neural networks, which we could utilize for video game playing. The aim of this thesis is to find a suitable fitness evaluation and improve the plasticity of evolved neural networks, as well as comparing the performance and general video game playing abilities of established neuroevolution methods. Using Analog Genetic Encoding we implem
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Robinson, Andrew Locke. "Evolving Neural Networks Through Random Augmentation and Sexual Reproduction." University of Akron / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=akron1606912380236988.

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Bahceci, Erkin. "Competitive multi-agent search." Thesis, 2014. http://hdl.handle.net/2152/28368.

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While evolutionary computation is well suited for automatic discovery in engineering, it can also be used to gain insight into how humans and organizations could perform more effectively. Using a real-world problem of innovation search in organizations as the motivating example, this dissertation formalizes human creative problem solving as competitive multi-agent search. It differs from existing single-agent and team-search problems in that the agents interact through knowledge of other agents' searches and through the dynamic changes in the search landscape caused by these searches. The main
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Piastka, Konrad. "Deep Semantic Learning Machine Initial design and experiments." Master's thesis, 2021. http://hdl.handle.net/10362/123465.

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Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics<br>Computer vision is an interdisciplinary scientific field that allows the digital world to interact with the real world. It is one of the fastest-growing and most important areas of data science. Applications are endless, given various tasks that can be solved thanks to the advances in the computer vision field. Examples of types of tasks that can be solved thanks to computer vision models are: image analysis, object detection, image transformation, and image generation.
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Book chapters on the topic "NeuroEvolution of Augmenting Topologies"

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Arellano, William R., Paul A. Silva, Maria F. Molina, Saulo Ronquillo, and Francisco Ortega-Zamorano. "Red-Black Tree Based NeuroEvolution of Augmenting Topologies." In Advances in Computational Intelligence. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20518-8_56.

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Pastorek, Lukas, and Michael O’Neill. "Historical Markings in Neuroevolution of Augmenting Topologies Revisited." In Theory and Practice of Natural Computing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71069-3_19.

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Manning, Timmy, and Paul Walsh. "Automatic Task Decomposition for the NeuroEvolution of Augmenting Topologies (NEAT) Algorithm." In Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29066-4_1.

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Calimeri, Francesco, Aldo Marzullo, Claudio Stamile, and Giorgio Terracina. "Blood Vessel Segmentation in Retinal Fundus Images Using Hypercube NeuroEvolution of Augmenting Topologies (HyperNEAT)." In Quantifying and Processing Biomedical and Behavioral Signals. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95095-2_17.

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Drchal, Jan, and Miroslav Šnorek. "Genetic Programming of Augmenting Topologies for Hypercube-Based Indirect Encoding of Artificial Neural Networks." In Advances in Intelligent Systems and Computing. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-32922-7_7.

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Ikonen, Teemu J., and Iiro Harjunkoski. "Decision-making of online rescheduling procedures using neuroevolution of augmenting topologies." In Computer Aided Chemical Engineering. Elsevier, 2019. http://dx.doi.org/10.1016/b978-0-12-818634-3.50197-1.

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Conference papers on the topic "NeuroEvolution of Augmenting Topologies"

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Pereira, Andre Grahl, and Adriano Petry. "Data assimilation using NeuroEvolution of Augmenting Topologies." In 2012 International Joint Conference on Neural Networks (IJCNN 2012 - Brisbane). IEEE, 2012. http://dx.doi.org/10.1109/ijcnn.2012.6252622.

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Kadish, David. "Clustering sensory inputs using NeuroEvolution of augmenting topologies." In GECCO '18: Genetic and Evolutionary Computation Conference. ACM, 2018. http://dx.doi.org/10.1145/3205651.3205771.

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Sohangir, Soroosh, Shahram Rahimi, and Bidyut Gupta. "Optimized feature selection using NeuroEvolution of Augmenting Topologies (NEAT)." In 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS). IEEE, 2013. http://dx.doi.org/10.1109/ifsa-nafips.2013.6608379.

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Grisci, Bruno, and Marcio Dorn. "Predicting protein structural features with NeuroEvolution of Augmenting Topologies." In 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 2016. http://dx.doi.org/10.1109/ijcnn.2016.7727291.

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Wen, Ruoshi, Zixi Guo, Tong Zhao, Xiang Ma, Qiang Wang, and Zhaojun Wu. "Neuroevolution of augmenting topologies based musculor-skeletal arm neurocontroller." In 2017 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). IEEE, 2017. http://dx.doi.org/10.1109/i2mtc.2017.7969727.

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Ibrahim, Mohamed Yilmaz, Rangarajan Sridhar, T. V. Geetha, and S. S. Deepika. "Advances in Neuroevolution through Augmenting Topologies – A Case Study." In 2019 11th International Conference on Advanced Computing (ICoAC). IEEE, 2019. http://dx.doi.org/10.1109/icoac48765.2019.246825.

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Chen, Lin, and Damminda Alahakoon. "NeuroEvolution of Augmenting Topologies with Learning for Data Classification." In 2006 International Conference on Information and Automation. IEEE, 2006. http://dx.doi.org/10.1109/icinfa.2006.374100.

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Kristo, Teofebano, and Nur Ulfa Maulidevi. "Deduction of fighting game countermeasures using Neuroevolution of Augmenting Topologies." In 2016 International Conference on Data and Software Engineering (ICoDSE). IEEE, 2016. http://dx.doi.org/10.1109/icodse.2016.7936127.

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Marzullo, Aldo, Claudio Stamile, Giorgio Terracina, Francesco Calimeri, and Sabine Van Huffel. "A tensor-based mutation operator for Neuroevolution of Augmenting Topologies (NEAT)." In 2017 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2017. http://dx.doi.org/10.1109/cec.2017.7969376.

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Wallaschek, Kay Luis, Robin Klose, Lars Almon, and Matthias Hollick. "NEAT-TCP: Generation of TCP Congestion Control through Neuroevolution of Augmenting Topologies." In 2020 IEEE International Conference on Communications Workshops (ICC Workshops). IEEE, 2020. http://dx.doi.org/10.1109/iccworkshops49005.2020.9145446.

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