Giuseppe Cuccu
Machine learning is the art of adapting generic solvers to address specific problems.
Short Bio
I am a Senior Researcher in Machine Learning with the eXascale Infolab, University of Fribourg, Switzerland under prof. Philippe Cudré-Mauroux.
Since 2022, I am affiliated with the Human-IST Institute under prof. Denis Lalanne with the position of Maître Assistant, as Coordinator for the new interdisciplinary Specialized Master in Digital Neuroscience.
I also also teach the courses of Machine Learning (BSc in CS) and Data Analytics in Python (DigitalSkills) as Lecturer.
I obtained my Ph.D. in 2018 (after a non-linear career started in 2009) from the University of Fribourg, under the supervision of prof. Philippe Cudré-Mauroux.
Prior to that I worked for different start-ups (and founded one), and as Research Assistant with both the Dalle Molle Institute for Artificial Intelligence (5y) and with the ICLS group at ZHAW (1y).
I completed both my BSc and MSc (Summa cum Laude) in Computer Science at the University of Milano-Bicocca, in Milan, Italy in 2009.
I have been involved in Machine Learning research since 2008 – for example in
Black-Box Optimization,
Neural Networks,
Neuroevolution and
Reinforcement Learning Control,
and addressing challenging real-world applications subject to strict data constraints (low quality / quantity).
Neural Networks
Parametrized generic function approximators.
Depending on their parametrization, neural networks are capable of approximating (theoretically) any computable function. This correlates with the network size, both in number of neurons and number of layers.
Their graph representation makes them easy to design and extend for humans, while unfortunately posing serious limitations to Architecture Search, i.e. automatically designing optimal network architectures.
This model has gained the spotlight over the past years through the results achieved by Deep Learning, which trains deep (i.e. large, multi-layer) networks using gradient descent supervised learning algorithms.
The main limitation of this algorithm is the Vanishing Gradient, where the first layers of the network receive less and lower quality gradient information than the last layers and thus require proportionally much more data as the network gets deeper.
This is a problem in the (many) real world scenarios where Data Scarcity is unavoidable, i.e. where labeled data is unavailable (e.g. direct policy search reinforcement learning) or difficult to obtain in significant quantities (e.g. medical applications).
Alternative training methods such as Black-Box Optimization supersede these limitations.
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Black-Box Optimization
Field studying parameter search algorithms that do not make assumptions on the application of the parameter set.
The main advantage of these methods is that they can be used as learning algorithms in any context without assumptions or requirements. The other side of the coin is that they are computationally expensive, and often limited in results when applied improperly.
A classic example of BBO is Evolutionary Algorithms, which search for the values for the parameters set to optimize a specific objective function, without knowing (nor needing access to) its internal workings.
Progress is based on (i) sampling the parameters space, (ii) computing the value of the objective function over the samples, then (iii) approximating the objective function's gradient over the samples, and (iv) generate new candidate solutions to maximize expected fitness.
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Neuroevolution
A technique to train Neural Networks using Evolutionary Algorithms.
EAs search in the space of network parameters to optimize the network performance on a task, solely led by a fitness function that scores the performance of candidate solutions.
This corresponds to directly search in the space of neural networks, optimizing an unrestricted scoring function which can represent the problem to arbitrary precision and complexity, in turn making Neuroevolution directly applicable to reinforcement learning control problems, as a method for Direct Policy Search.
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Reinforcement Learning
A learning paradigm where the only direction for training is given by a score function over candidate solutions.
Not to be confused with the Classical Reinforcement Learning framework, which is the most common approach to this class of problems (e.g. Deep Reinforcement Learning).
While Supervised Learning methods expect a set of labeled data "to copy from", Reinforcement Learning only provides an objective evaluation for candidate solutions and leaves the algorithm to figure out how to optimize future solutions towards improving scores.
This is the way all life learns most of its behaviors, by trial and error, the only notable exception being instincts and (to a somewhat limited effect) studying/teaching.
Notably, Black-Box Optimization fits this paradigm without further manipulations and setups.
×
Research Interests
My overarching research goal is to solve
Reinforcement Learning Continuous Control,
by any means necessary. This implies creating and improving methods uniquely based on their ability to scale to real-world conditions.
[Click here to read why.]
Reinforcement Learning Continuous Control
A class of problems requiring sequential decision making over continuous time.
At any given moment, the system provides an observation based on its internal (typically hidden) status.
A controller is then tasked with deciding the next action based on the given observation and possibly its own internal state (memory).
Most real-world interactive systems belong to this category, such as robotic control, autonomous driving and even videogame playing.
It is typically hard to approach these problems from a supervised learning perspective, as "correct" examples of control are hard to obtain and limiting in the range of skills they can teach. Integrating exploration of novel behaviors is also fairly complex under this paradigm.
A direct Reinforcement Learning approach however can be more natural in this context: focus on approximating the "control process", taking an observation as input and outputting the decide action is (arguably) a more direct approximation of the way all life interacts with the world, and typically leads to simpler and more efficient controllers.
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Why Reinforcement Learning Continuous Control
There is a big "elephant in the room" in Machine Learning: we still wish (and often promise) to solve challenging real-world control applications;
yet despite our extraordinary academic results, most control tasks today still rely on human operators.
With electronic support systems have become ubiquitous, heavy machinery are as easy to control remotely as more classic robotic platforms;
and most Machine Learning methods are now freely accessible online, anyone can do it.
So where is my self-driving car?
Truth is, we do not have yet a method that can teach a task to a machine in the same way that we teach people.
We keep creating new, ad-hoc procedures to teach stuff we already know, but this approach simply does not scale: we cannot bend the world to laboratory conditions, autonomous machines need to coexist by our side.
We need to shed all of the constraints and limitations that shackle current mainstream methods.
We need a radical paradigm shift in Machine Learning.
We need machines that learn by interacting with an unbound, partially observable and noisy world. We have no correct control examples for each possible observation, no reward for each atomic action taken. Control functions are almost never differentiable nor continuous, and human memory is immensely complex.
We try, we fail, we pick ourselves up by our own intrinsic motivation, without oracles. We improve skills in isolation and adapt to different contexts, but to share our finding we can only speak or demonstrate to each other.
And yet, we manage.
We need machines that can manage, too.
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Some specific topics and challenges include:
- Alternative models to classic Neural Networks, with more desirable features, such as:
- Native Architecture Search for model complexity scalability
- Sub-task specialization and split responsibility
- Advanced memory implements, with focus on object permanence and expert knowledge integration
- Human interpretability, both for memory and control model, for high-responsibility applications
- Alternative model training methods, including:
- High parallelization and scalability for Black-Box Optimization (check out DiBB!)
- Adaptation of Deep Learning to problems with limited data availability
- Advanced reward/fitness shaping, focusing on:
- Competing intrinsic motivation signals
- Self-balancing explicit exploration
- Hyperparameters meta-search
Selected Awards
- Best Paper Award. International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2019).
- Best Paper Award Runner-Up. Foundation of Digital Games (FDG 2013).
- Best Paper Award. International Conference on Evolutionary Computation (ICEC 2009).
Teaching
[SA22]: Data Analytics in Python [Moodle]
Archive
[SP22]: Machine Learning [Moodle]
[SP21]: Machine Learning [Moodle]
[SP20]: Machine Learning [Moodle]
Student Thesis
I supervise a limited number of exceptional students depending on current availability. I typically ask to take my course on Machine Learning (or equivalent preparation) first, as it simplifies communication and significantly lowers the time and effort to graduation.
- Available topics
- Real-world control using Direct Policy Search for Reinforcement Learning
- Alternative models to Neural Networks, sidestepping Deep Learning’s limitations
- Efficient distributed Evolutionary Computation: scaling the performance of sophisticated searching in continuous parameter space
- Augmenting control models with new memory implementations focusing on Object Permanence
- Prostate cancer detection using Deep Learning: overcoming data scarcity in medical imaging applications – collaboration with the Hôpital Cantonal de Fribourg [H-FR]
- More available on request.
- Ongoing
- MSc: Jonas Fontana:
Sophisticated control by approximating policies with large non-NN models
- Alumni
- 2022.08 MSc - Corina Masanti:
Alternative Models for Direct Policy Search in Reinforcement Learning Control Problems
[pdf]
- 2022.08 MSc - Fabien Vorpe:
Neuroevolution Applications for the DiBB Framework
[pdf]
- 2022.06 MSc - Nicolas Roguet:
Controlling Complex Unstable Robotic Systems Using Direct Policy Search
[pdf]
- 2022.02 BSc - Christophe Broillet:
Pre-Processing Segmentation Datasets for the Hydra Framework
[pdf]
- 2021.08 MSc - Luca Rolshoven:
Study and Extension of the DiBB Framework for Distributing Black-Box Optimization
[pdf]
- 2020.12 MSc - Jiyoung Lee:
P-Hydra: Bridging Transfer Learning And Multitask Learning
[pdf]
- 2020.03 MSc - Johan Jobin & Julien Clément:
Prostate Cancer Classification: A Transfer Learning Approach to Integrate Information From Diverse Body Parts
[pdf]
- 2019.11 BSc - David Bucher:
Data Preparation and Analysis in Support to Cheating Detection: The Case for Economic Momentum in CS:GO
[pdf]
Publications
- Giuseppe Cuccu, Luca Rolshoven, Fabien Vorpe, Philippe Cudré-Mauroux, and Tobias Glasmachers. “DiBB: Distributing Black-Box Optimization.” In Proceedings of the Genetic and Evolutionary Computation Conference, 341–49. GECCO ’22. New York, NY, USA: Association for Computing Machinery, 2022. Bibtex PDF
DiBB: Distributing Black-Box Optimization
Giuseppe Cuccu, Luca Rolshoven, Fabien Vorpe, Philippe Cudré-Mauroux, and Tobias Glasmachers. “DiBB: Distributing Black-Box Optimization.” In Proceedings of the Genetic and Evolutionary Computation Conference, 341–49. GECCO ’22. New York, NY, USA: Association for Computing Machinery, 2022.
@inproceedings{cuccu2022gecco,
author = {Cuccu, Giuseppe and Rolshoven, Luca and Vorpe, Fabien and Cudr\'{e}-Mauroux, Philippe and Glasmachers, Tobias},
title = {{DiBB}: Distributing Black-Box Optimization},
year = {2022},
isbn = {9781450392372},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
doi = {10.1145/3512290.3528764},
booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference},
pages = {341–349},
numpages = {9},
keywords = {parallelization, neuroevolution, evolution strategies, distributed algorithms, black-box optimization},
location = {Boston, Massachusetts},
series = {GECCO '22},
url = {https://exascale.info/assets/pdf/cuccu2022gecco.pdf}
}
×
- Ines Arous, Ljiljana Dolamic, Jie Yang, Akansha Bhardwaj, Giuseppe Cuccu, and Philippe Cudré-Mauroux. “MARTA: Leveraging Human Rationales for Explainable Text Classification.” In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI 2021). A Virtual Conference, 2021. Bibtex Slides PDF Appendix Code Poster
MARTA: Leveraging Human Rationales for Explainable Text Classification
Ines Arous, Ljiljana Dolamic, Jie Yang, Akansha Bhardwaj, Giuseppe Cuccu, and Philippe Cudré-Mauroux. “MARTA: Leveraging Human Rationales for Explainable Text Classification.” In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI 2021). A Virtual Conference, 2021.
@inproceedings{arous2021aaai,
title = {MARTA: Leveraging Human Rationales for Explainable Text Classification},
author = {Arous, Ines and Dolamic, Ljiljana and Yang, Jie and Bhardwaj, Akansha and Cuccu, Giuseppe and Cudr{\'e}-Mauroux, Philippe},
booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence (AAAI 2021)},
year = {2021},
address = {A Virtual Conference},
url = {https://exascale.info/assets/pdf/arous2021aaai.pdf},
appendix = {https://exascale.info/assets/pdf/arous2020AAAI-sm.pdf},
note = {https://exascale.info/assets/pdf/arous2021aaai_slides.pdf},
poster = {https://exascale.info/assets/pdf/arous2021aaai_poster.pdf},
code = {https://github.com/eXascaleInfolab/MARTA}
}
×
- Giuseppe Cuccu, Julian Togelius, and Philippe Cudré-Mauroux. “Playing Atari with Few Neurons.” Autonomous Agents and Multi-Agent Systems 35, no. 2 (2021): 1–23. Bibtex PDF
Playing Atari with few neurons
Giuseppe Cuccu, Julian Togelius, and Philippe Cudré-Mauroux. “Playing Atari with Few Neurons.” Autonomous Agents and Multi-Agent Systems 35, no. 2 (2021): 1–23.
@article{cuccu2021playing,
title = {Playing Atari with few neurons},
author = {Cuccu, Giuseppe and Togelius, Julian and Cudr{\'e}-Mauroux, Philippe},
journal = {Autonomous Agents and Multi-Agent Systems},
volume = {35},
number = {2},
pages = {1--23},
year = {2021},
doi = {10.1007/s10458-021-09497-8},
url = {https://exascale.info/assets/pdf/cuccu2021jaamas.pdf},
publisher = {Springer}
}
×
- Giuseppe Cuccu, Julian Togelius, and Philippe Cudré-Mauroux. “Playing Atari with Six Neurons (Extended Abstract).” In Proceedings of the Twenty-Ninth International Joint Conference On
Artificial Intelligence, IJCAI-20, edited by Christian Bessiere, 4711–15. International Joint Conferences on Artificial Intelligence Organization, 2020. Bibtex Slides PDF
Playing Atari with Six Neurons (Extended Abstract)
Giuseppe Cuccu, Julian Togelius, and Philippe Cudré-Mauroux. “Playing Atari with Six Neurons (Extended Abstract).” In Proceedings of the Twenty-Ninth International Joint Conference On
Artificial Intelligence, IJCAI-20, edited by Christian Bessiere, 4711–15. International Joint Conferences on Artificial Intelligence Organization, 2020.
@inproceedings{cuccu2020ijcai,
title = {Playing Atari with Six Neurons (Extended Abstract)},
author = {Cuccu, Giuseppe and Togelius, Julian and Cudré-Mauroux, Philippe},
booktitle = {Proceedings of the Twenty-Ninth International Joint Conference on
Artificial Intelligence, {IJCAI-20}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Bessiere, Christian},
pages = {4711--4715},
year = {2020},
month = jul,
note = {Sister Conferences Best Papers},
doi = {10.24963/ijcai.2020/651},
url = {https://exascale.info/assets/pdf/cuccu2020ijcai.pdf}
}
×
- Giuseppe Cuccu, Johan Jobin, Julien Clément, Akansha Bhardwaj, Carolin Reischauer, Harriet Thöny, and Philippe Cudré-Mauroux. “Hydra: Cancer Detection Leveraging Multiple Heads and Heterogeneous Datasets.” In 2020 IEEE International Conference on Big Data, BigData, 2020. Bibtex PDF
Hydra: Cancer Detection Leveraging Multiple Heads and Heterogeneous Datasets
Giuseppe Cuccu, Johan Jobin, Julien Clément, Akansha Bhardwaj, Carolin Reischauer, Harriet Thöny, and Philippe Cudré-Mauroux. “Hydra: Cancer Detection Leveraging Multiple Heads and Heterogeneous Datasets.” In 2020 IEEE International Conference on Big Data, BigData, 2020.
@inproceedings{cuccu2020bigdata,
title = {{Hydra}: Cancer Detection Leveraging Multiple Heads and Heterogeneous Datasets},
author = {Cuccu, Giuseppe and Jobin, Johan and Clément, Julien and Bhardwaj, Akansha and Reischauer, Carolin and Thöny, Harriet and Cudré-Mauroux, Philippe},
booktitle = {2020 {IEEE} International Conference on Big Data, {BigData}},
year = {2020},
month = dec,
url = {https://exascale.info/assets/pdf/cuccu2020bigdata.pdf}
}
×
- Declan Oller, Tobias Glasmachers, and Giuseppe Cuccu. “Analyzing Reinforcement Learning Benchmarks with Random Weight Guessing.” In Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems. International Foundation for Autonomous Agents and Multiagent Systems, 2020. Bibtex PDF
Analyzing Reinforcement Learning Benchmarks with Random Weight Guessing
Declan Oller, Tobias Glasmachers, and Giuseppe Cuccu. “Analyzing Reinforcement Learning Benchmarks with Random Weight Guessing.” In Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems. International Foundation for Autonomous Agents and Multiagent Systems, 2020.
@inproceedings{oller2020aamas,
title = {Analyzing Reinforcement Learning Benchmarks with Random Weight Guessing},
author = {Oller, Declan and Glasmachers, Tobias and Cuccu, Giuseppe},
booktitle = {Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems},
year = {2020},
organization = {International Foundation for Autonomous Agents and Multiagent Systems},
url = {https://exascale.info/assets/pdf/oller2020aamas.pdf}
}
×
- Giuseppe Cuccu, Julian Togelius, and Philippe Cudré-Mauroux. “Playing Atari with Six Neurons.” In Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, 998–1006. International Foundation for Autonomous Agents and Multiagent Systems, 2019. Bibtex PDF
Playing Atari with six neurons
Giuseppe Cuccu, Julian Togelius, and Philippe Cudré-Mauroux. “Playing Atari with Six Neurons.” In Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, 998–1006. International Foundation for Autonomous Agents and Multiagent Systems, 2019.
@inproceedings{cuccu2019aamas,
title = {Playing {A}tari with six neurons},
author = {Cuccu, Giuseppe and Togelius, Julian and Cudr{\'e}-Mauroux, Philippe},
booktitle = {Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems},
pages = {998--1006},
year = {2019},
organization = {International Foundation for Autonomous Agents and Multiagent Systems},
url = {https://exascale.info/assets/pdf/cuccu2019aamas.pdf}
}
×
- Giuseppe Cuccu. “Extending the Applicability of Neuroevolution.” PhD thesis, Université de Fribourg, 2018. Bibtex PDF
Extending the Applicability of Neuroevolution
Giuseppe Cuccu. “Extending the Applicability of Neuroevolution.” PhD thesis, Université de Fribourg, 2018.
@phdthesis{cuccu2018phd,
author = {Cuccu, Giuseppe},
title = {Extending the Applicability of Neuroevolution},
school = {Universit{\'e} de Fribourg},
address = {Fribourg, Switzerland},
year = {2018},
url = {https://exascale.info/assets/pdf/cuccu2018phd.pdf}
}
×
- Giuseppe Cuccu, Somayeh Danafar, Philippe Cudré-Mauroux, Martin Gassner, Stefano Bernero, and Krzysztof Kryszczuk. “A Data-Driven Approach to Predict NOx-Emissions of Gas Turbines.” In 2017 IEEE International Conference on Big Data, BigData 2017, Boston,
MA, USA, December 11-14, 2017, 1283–88, 2017. Bibtex PDF
A data-driven approach to predict NOx-emissions of gas turbines
Giuseppe Cuccu, Somayeh Danafar, Philippe Cudré-Mauroux, Martin Gassner, Stefano Bernero, and Krzysztof Kryszczuk. “A Data-Driven Approach to Predict NOx-Emissions of Gas Turbines.” In 2017 IEEE International Conference on Big Data, BigData 2017, Boston,
MA, USA, December 11-14, 2017, 1283–88, 2017.
@inproceedings{cuccu2017bigdata,
author = {Cuccu, Giuseppe and Danafar, Somayeh and Cudr{\'e}-Mauroux, Philippe and Gassner, Martin and Bernero, Stefano and Kryszczuk, Krzysztof},
title = {A data-driven approach to predict NOx-emissions of gas turbines},
booktitle = {2017 {IEEE} International Conference on Big Data, BigData 2017, Boston,
MA, USA, December 11-14, 2017},
pages = {1283--1288},
year = {2017},
doi = {10.1109/BigData.2017.8258056},
url = {https://exascale.info/assets/pdf/cuccu2017bigdata.pdf}
}
×
- Jan Koutník, Giuseppe Cuccu, Jürgen Schmidhuber, and Faustino Gomez. “Evolving Large-Scale Neural Networks for Vision-Based Reinforcement Learning.” In Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation, 1061–68. GECCO ’13. ACM, 2013. Bibtex PDF
Evolving Large-scale Neural Networks for Vision-based Reinforcement Learning
Jan Koutník, Giuseppe Cuccu, Jürgen Schmidhuber, and Faustino Gomez. “Evolving Large-Scale Neural Networks for Vision-Based Reinforcement Learning.” In Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation, 1061–68. GECCO ’13. ACM, 2013.
@inproceedings{koutnik2013gecco,
location = {New York, {NY}, {USA}},
title = {Evolving Large-scale Neural Networks for Vision-based Reinforcement Learning},
isbn = {978-1-4503-1963-8},
url = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.451.760&rep=rep1&type=pdf},
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}
×
- Jan Koutník, Giuseppe Cuccu, Jürgen Schmidhuber, and Faustino Gomez. “Evolving Large-Scale Neural Networks for Vision-Based TORCS.” In Foundations of Digital Games (FDG), 206–12, 2013. Bibtex PDF
Evolving Large-Scale Neural Networks for Vision-Based TORCS
Jan Koutník, Giuseppe Cuccu, Jürgen Schmidhuber, and Faustino Gomez. “Evolving Large-Scale Neural Networks for Vision-Based TORCS.” In Foundations of Digital Games (FDG), 206–12, 2013.
@inproceedings{koutnik2013fdg,
location = {Chania, Crete, {GR}},
title = {Evolving Large-Scale Neural Networks for Vision-Based {TORCS}},
isbn = {{ISBN} 978-0-9913982-0-1},
url = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.654.9135&rep=rep1&type=pdf},
pages = {206--212},
booktitle = {Foundations of Digital Games (FDG)},
author = {Koutník, Jan and Cuccu, Giuseppe and Schmidhuber, Jürgen and Gomez, Faustino},
year = {2013}
}
×
- Giuseppe Cuccu, and Faustino Gomez. “Block Diagonal Natural Evolution Strategies.” In Parallel Problem Solving from Nature - PPSN XII, 488–97. Springer, Berlin, Heidelberg, 2012. Bibtex PDF
Block Diagonal Natural Evolution Strategies
Giuseppe Cuccu, and Faustino Gomez. “Block Diagonal Natural Evolution Strategies.” In Parallel Problem Solving from Nature - PPSN XII, 488–97. Springer, Berlin, Heidelberg, 2012.
@inproceedings{cuccu2012ppsn,
title = {Block Diagonal Natural Evolution Strategies},
url = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.308.655&rep=rep1&type=pdf},
doi = {10.1007/978-3-642-32964-7_49},
eventtitle = {International Conference on Parallel Problem Solving from Nature},
pages = {488--497},
booktitle = {Parallel Problem Solving from Nature - {PPSN} {XII}},
publisher = {Springer, Berlin, Heidelberg},
author = {Cuccu, Giuseppe and Gomez, Faustino},
year = {2012},
langid = {english}
}
×
- Vincent Graziano, Tobias Glasmachers, Tom Schaul, Leo Pape, Giuseppe Cuccu, Jürgen Leitner, and Jürgen Schmidhuber. “Artificial Curiosity for Autonomous Space Exploration.” Acta Futura 4 (2011): 41–51. Bibtex PDF
Artificial Curiosity for Autonomous Space Exploration
Vincent Graziano, Tobias Glasmachers, Tom Schaul, Leo Pape, Giuseppe Cuccu, Jürgen Leitner, and Jürgen Schmidhuber. “Artificial Curiosity for Autonomous Space Exploration.” Acta Futura 4 (2011): 41–51.
@article{graziano2011acta,
title = {Artificial Curiosity for Autonomous Space Exploration},
volume = {4},
url = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.225.3031&rep=rep1&type=pdf},
pages = {41--51},
journaltitle = {Acta Futura},
author = {Graziano, Vincent and Glasmachers, Tobias and Schaul, Tom and Pape, Leo and Cuccu, Giuseppe and Leitner, Jürgen and Schmidhuber, Jürgen},
year = {2011}
}
×
- Leonardo Vanneschi, and Giuseppe Cuccu. “Reconstructing Dynamic Target Functions by Means of Genetic Programming Using Variable Population Size.” In Computational Intelligence, 343:121–34. Springer, Berlin, Heidelberg, 2011. Bibtex PDF
Reconstructing Dynamic Target Functions by Means of Genetic Programming Using Variable Population Size
Leonardo Vanneschi, and Giuseppe Cuccu. “Reconstructing Dynamic Target Functions by Means of Genetic Programming Using Variable Population Size.” In Computational Intelligence, 343:121–34. Springer, Berlin, Heidelberg, 2011.
@incollection{vanneschi2011computational,
title = {Reconstructing Dynamic Target Functions by Means of Genetic Programming Using Variable Population Size},
volume = {343},
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pages = {121--134},
booktitle = {Computational Intelligence},
publisher = {Springer, Berlin, Heidelberg},
author = {Vanneschi, Leonardo and Cuccu, Giuseppe},
year = {2011},
langid = {english},
doi = {10.1007/978-3-642-20206-3_8}
}
×
- Giuseppe Cuccu, Matthew Luciw, Jürgen Schmidhuber, and Faustino Gomez. “Intrinsically Motivated Neuroevolution for Vision-Based Reinforcement Learning.” In Development and Learning (ICDL), 2011 IEEE International Conference On, 2:1–7. IEEE, 2011. Bibtex PDF
Intrinsically Motivated Neuroevolution for Vision-based Reinforcement Learning
Giuseppe Cuccu, Matthew Luciw, Jürgen Schmidhuber, and Faustino Gomez. “Intrinsically Motivated Neuroevolution for Vision-Based Reinforcement Learning.” In Development and Learning (ICDL), 2011 IEEE International Conference On, 2:1–7. IEEE, 2011.
@inproceedings{cuccu2011icdl,
title = {Intrinsically Motivated Neuroevolution for Vision-based Reinforcement Learning},
volume = {2},
url = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.308.3917&rep=rep1&type=pdf},
pages = {1--7},
booktitle = {Development and Learning ({ICDL}), 2011 {IEEE} International Conference on},
publisher = {{IEEE}},
author = {Cuccu, Giuseppe and Luciw, Matthew and Schmidhuber, Jürgen and Gomez, Faustino},
year = {2011},
keywords = {artificial evolution, Computer vision, continuous reinforcement learning, embedded agents, high-dimensional visual images, intrinsically motivated neuroevolution, learning (artificial intelligence), neural networks, recurrent neural nets, recurrent neural network controllers, Tin, unsupervised sensory preprocessor, vision-based reinforcement learning, Yttrium}
}
×
- Giuseppe Cuccu, and Faustino Gomez. “When Novelty Is Not Enough.” In Applications of Evolutionary Computation, 234–43. Springer, Berlin, Heidelberg, 2011. Bibtex PDF
When Novelty Is Not Enough
Giuseppe Cuccu, and Faustino Gomez. “When Novelty Is Not Enough.” In Applications of Evolutionary Computation, 234–43. Springer, Berlin, Heidelberg, 2011.
@inproceedings{cuccu2011evostar,
title = {When Novelty Is Not Enough},
url = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.383.826&rep=rep1&type=pdf},
doi = {10.1007/978-3-642-20525-5_24},
eventtitle = {European Conference on the Applications of Evolutionary Computation},
pages = {234--243},
booktitle = {Applications of Evolutionary Computation},
publisher = {Springer, Berlin, Heidelberg},
author = {Cuccu, Giuseppe and Gomez, Faustino},
year = {2011},
langid = {english}
}
×
- Giuseppe Cuccu, Faustino Gomez, and Tobias Glasmachers. “Novelty-Based Restarts for Evolution Strategies.” In Evolutionary Computation (CEC), 2011 IEEE Congress On, 158–63. IEEE, 2011. Bibtex PDF
Novelty-based Restarts for Evolution Strategies
Giuseppe Cuccu, Faustino Gomez, and Tobias Glasmachers. “Novelty-Based Restarts for Evolution Strategies.” In Evolutionary Computation (CEC), 2011 IEEE Congress On, 158–63. IEEE, 2011.
@inproceedings{cuccu2011cec,
title = {Novelty-based Restarts for Evolution Strategies},
url = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.308.240&rep=rep1&type=pdf},
pages = {158--163},
booktitle = {Evolutionary Computation ({CEC}), 2011 {IEEE} Congress on},
publisher = {{IEEE}},
author = {Cuccu, Giuseppe and Gomez, Faustino and Glasmachers, Tobias},
year = {2011},
keywords = {black box optimization, black-box optimization, black box search algorithm, Convergence, Covariance matrix, Equations, evolutionary computation, evolution strategies, Monte Carlo methods, novelty-based restarts, novelty search, optimisation, Optimization, restart strategies, search problems, search space, Switches}
}
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- Leonardo Vanneschi, and Giuseppe Cuccu. “Variable Size Population for Dynamic Optimization with Genetic Programming.” In Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, 1895–96. GECCO ’09. ACM, 2009. Bibtex PDF
Variable Size Population for Dynamic Optimization with Genetic Programming
Leonardo Vanneschi, and Giuseppe Cuccu. “Variable Size Population for Dynamic Optimization with Genetic Programming.” In Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, 1895–96. GECCO ’09. ACM, 2009.
@inproceedings{vanneschi2009gecco,
author = {Vanneschi, Leonardo and Cuccu, Giuseppe},
location = {New York, {NY}, {USA}},
title = {Variable Size Population for Dynamic Optimization with Genetic Programming},
isbn = {978-1-60558-325-9},
url = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.205.7910&rep=rep1&type=pdf},
doi = {10.1145/1569901.1570222},
series = {{GECCO} '09},
pages = {1895--1896},
booktitle = {Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation},
publisher = {{ACM}},
year = {2009},
keywords = {dynamic optimization, genetic algorithms, genetic programming, genetic programming, Poster, variable size populations}
}
×
- Leonardo Vanneschi, and Giuseppe Cuccu. “A Study of Genetic Programming Variable Population Size for Dynamic Optimization Problems.” In International Conference on Evolutionary Computation (ICEC), 119–26, 2009. Bibtex PDF
A Study of Genetic Programming Variable Population Size for Dynamic Optimization Problems
Leonardo Vanneschi, and Giuseppe Cuccu. “A Study of Genetic Programming Variable Population Size for Dynamic Optimization Problems.” In International Conference on Evolutionary Computation (ICEC), 119–26, 2009.
@inproceedings{vanneschi2009icec,
author = {Vanneschi, Leonardo and Cuccu, Giuseppe},
location = {Madeira, Portugal},
title = {A Study of Genetic Programming Variable Population Size for Dynamic Optimization Problems},
url = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.205.7483&rep=rep1&type=pdf},
pages = {119--126},
booktitle = {International Conference on Evolutionary Computation (ICEC)},
year = {2009},
keywords = {genetic algorithms, genetic programming}
}
×