Difference between revisions of "Alan J. Lockett"
GerdIsenberg (talk | contribs) (Created page with "'''Home * People * Alan J. Lockett''' FILE:alan-lockett.jpg|border|right|thumb|link=http://nn.cs.utexas.edu/?alanlockett| Alan J. Lockett <ref>[http://nn....") |
GerdIsenberg (talk | contribs) |
||
| Line 1: | Line 1: | ||
'''[[Main Page|Home]] * [[People]] * Alan J. Lockett''' | '''[[Main Page|Home]] * [[People]] * Alan J. Lockett''' | ||
| − | [[FILE:alan-lockett.jpg|border|right|thumb|link=http://nn.cs.utexas.edu/?alanlockett| Alan J. Lockett <ref>[http://nn.cs.utexas.edu/?alanlockett | + | [[FILE:alan-lockett.jpg|border|right|thumb|link=http://nn.cs.utexas.edu/?alanlockett| Alan J. Lockett <ref>[http://nn.cs.utexas.edu/?alanlockett NNRG People - Alan J. Lockett]</ref> ]] |
'''Alan J. Lockett''',<br/> | '''Alan J. Lockett''',<br/> | ||
an American computer scientist, Ph.D. from [https://en.wikipedia.org/wiki/University_of_Texas_at_Austin University of Texas at Austin] under [[Risto Miikkulainen]]. | an American computer scientist, Ph.D. from [https://en.wikipedia.org/wiki/University_of_Texas_at_Austin University of Texas at Austin] under [[Risto Miikkulainen]]. | ||
| − | In his thesis | + | In his thesis on [[Learning|machine learning]] and [https://en.wikipedia.org/wiki/Global_optimization global optimization], he introduced the [https://en.wikipedia.org/wiki/Martingale_(probability_theory) martingale] based '''Evolutionary Annealing''', |
which resembles an [[Genetic Programming#EvolutionaryAlgorithms|evolutionary algorithms]] to approximates samples from an increasingly sharp | which resembles an [[Genetic Programming#EvolutionaryAlgorithms|evolutionary algorithms]] to approximates samples from an increasingly sharp | ||
[https://en.wikipedia.org/wiki/Boltzmann_distribution Boltzmann distribution], asymptotically focusing on the global optima. | [https://en.wikipedia.org/wiki/Boltzmann_distribution Boltzmann distribution], asymptotically focusing on the global optima. | ||
| − | '''Neuroannealing''' applies evolutionary annealing to evolve and learn [[Neural Networks|neural networks]]. | + | '''Neuroannealing''' applies evolutionary annealing to evolve and learn [[Neural Networks|neural networks]] <ref>[[Alan J. Lockett]] ('''2012'''). ''General-Purpose Optimization Through Information Maximization''. Ph.D. thesis, [https://en.wikipedia.org/wiki/University_of_Texas_at_Austin University of Texas at Austin], advisor [[Risto Miikkulainen]]</ref>. His further research interests include [https://en.wikipedia.org/wiki/Humanoid_robot humanoid robotics], [[Deep Learning|deep learning]] and [[Opponent Model Search|opponent modelling]] in [[Games|games]]. |
| − | His further research interests include [https://en.wikipedia.org/wiki/Humanoid_robot humanoid robotics], [[Deep Learning|deep learning]] and [[Opponent Model Search|opponent modelling]] in [[Games|games]]. | ||
=Selected Publications= | =Selected Publications= | ||
Revision as of 20:35, 13 July 2020
Home * People * Alan J. Lockett
Alan J. Lockett [1]
Alan J. Lockett,
an American computer scientist, Ph.D. from University of Texas at Austin under Risto Miikkulainen.
In his thesis on machine learning and global optimization, he introduced the martingale based Evolutionary Annealing,
which resembles an evolutionary algorithms to approximates samples from an increasingly sharp
Boltzmann distribution, asymptotically focusing on the global optima.
Neuroannealing applies evolutionary annealing to evolve and learn neural networks [2]. His further research interests include humanoid robotics, deep learning and opponent modelling in games.
Selected Publications
- Alan J. Lockett, Charles L. Chen, Risto Miikkulainen (2007). Evolving Explicit Opponent Models for Game Play. GECCO 2007
- Alan J. Lockett, Risto Miikkulainen (2008). Evolving Opponent Models for Texas Hold 'Em. CIG 2008
2010 ...
- Alan J. Lockett, Risto Miikkulainen (2011). Real-Space Evolutionary Annealing. GECCO 2011
- Alan J. Lockett (2012). General-Purpose Optimization Through Information Maximization. Ph.D. thesis, University of Texas at Austin, advisor Risto Miikkulainen, pdf
- Alan J. Lockett, Risto Miikkulainen (2013). A Measure-Theoretic Analysis of Stochastic Optimization. FOGA 2013
- Alan J. Lockett (2014). Model-optimal optimization by solving bellman equations. GECCO 2014 [5]
- Alan J. Lockett, Risto Miikkulainen (2014). Evolutionary Annealing: Global Optimization in Arbitrary Measure Spaces. Journal of Global Optimization, Vol. 58
- Marijn F. Stollenga, Alan J. Lockett, Jürgen Schmidhuber (2015). The Natural Gradient as a control signal for a humanoid robot. Humanoids 2015
- Alan J. Lockett (2015). Insights From Adversarial Fitness Functions. FOGA 2015, pdf (extended abstract)
External Links
- Alan J Lockett's Optimization Research
- NNRG People - Alan J. Lockett
- Alan J Lockett - Google Scholar
References
- ↑ NNRG People - Alan J. Lockett
- ↑ Alan J. Lockett (2012). General-Purpose Optimization Through Information Maximization. Ph.D. thesis, University of Texas at Austin, advisor Risto Miikkulainen
- ↑ dblp: Alan J. Lockett
- ↑ Alan J Lockett - Google Scholar
- ↑ Bellman equation from Wikipedia
