R
Risto Miikkulainen
Researcher at University of Texas at Austin
Publications - 418
Citations - 18402
Risto Miikkulainen is an academic researcher from University of Texas at Austin. The author has contributed to research in topics: Artificial neural network & Neuroevolution. The author has an hindex of 59, co-authored 399 publications receiving 16649 citations. Previous affiliations of Risto Miikkulainen include Association for Computing Machinery & Toyota Motor Engineering & Manufacturing North America.
Papers
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Journal ArticleDOI
Evolving neural networks through augmenting topologies
TL;DR: Neural Evolution of Augmenting Topologies (NEAT) as mentioned in this paper employs a principled method of crossover of different topologies, protecting structural innovation using speciation, and incrementally growing from minimal structure.
Book ChapterDOI
Evolving Deep Neural Networks
Risto Miikkulainen,Jason Zhi Liang,Elliot Meyerson,Aditya Rawal,Fink Daniel E,Olivier Francon,Bala Raju,Hormoz Shahrzad,Arshak Navruzyan,Nigel Duffy,Babak Hodjat +10 more
TL;DR: An automated method, CoDeepNEAT, is proposed for optimizing deep learning architectures through evolution by extending existing neuroevolution methods to topology, components, and hyperparameters, which achieves results comparable to best human designs in standard benchmarks in object recognition and language modeling.
Proceedings Article
Intrusion Detection with Neural Networks
TL;DR: A backpropagation neural network called NNID (Neural Network Intrusion Detector) was trained in the identification task and tested experimentally on a system of 10 users, suggesting that learning user profiles is an effective way for detecting intrusions.
Journal ArticleDOI
A Taxonomy for artificial embryogeny
TL;DR: This taxonomy provides a unified context for long-term research in AE, so that implementation decisions can be compared and contrasted along known dimensions in the design space of embryogenic systems, and allows predicting how the settings of various AE parameters affect the capacity to efficiently evolve complex phenotypes.
Journal ArticleDOI
Designing neural networks through neuroevolution
TL;DR: This Review looks at several key aspects of modern neuroevolution, including large-scale computing, the benefits of novelty and diversity, the power of indirect encoding, and the field’s contributions to meta-learning and architecture search.