Searched for: subject%3A%22genetic%255C+programming%22
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de Groot, Patrick (author)
Current service and installation activities for offshore wind turbines are carried out by jack-up vessels, which eliminate wave disturbances to a large extent. The use of these vessels imposes several disadvantages, including high operational costs and the limitation of operating in restricted water depths. An alternative is a crane mounted on a...
master thesis 2024
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Verlinde, Lander (author)
The role of Unmanned Aerial Vehicles (UAVs), more commonly known as drones, in society continues to become more significant every day, both in everyday life and in military operations. The extent to which unmanned vehicles are used for both offensive as well as reconnaissance missions is at an all-time high. To expand the number of operational...
student report 2024
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Harrison, Joe (author), Virgolin, Marco (author), Alderliesten, T. (author), Bosman, P.A.N. (author)
The aim of Symbolic Regression (SR) is to discover interpretable expressions that accurately describe data. The accuracy of an expression depends on both its structure and coefficients. To keep the structure simple enough to be interpretable, effective coefficient optimisation becomes key. Gradient-based optimisation is clearly effective at...
conference paper 2023
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Azimzade, Farhad (author)
Inductive Program Synthesis is the problem of generating programs from<br/>a set of input-output examples. Since it can be reduced to the search problem in the space of programs, many search algorithms have been successfully<br/>applied to it over the years. This paper proposes, develops, and analyses<br/>a novel algorithm in the family of...
bachelor thesis 2022
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LU, Jingyi (author)
Inspired by the natural nervous system, synaptic plasticity rules are applied to train spiking neural networks. Different from learning algorithms such as propagation and evolution that are widely used to train spiking neural networks, synaptic plasticity rules learn the parameters with local information, making them suitable for online learning...
master thesis 2022
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Harrison, J. (author), Alderliesten, T. (author), Bosman, P.A.N. (author)
Genetic Programming (GP) can make an important contribution to explainable artificial intelligence because it can create symbolic expressions as machine learning models. Nevertheless, to be explainable, the expressions must not become too large. This may, however, limit their potential to be accurate. The re-use of subexpressions has the...
conference paper 2022
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Saberi, Saeid (author), Sadat Hosseini, Alireza (author), Yazdanifar, Fatemeh (author), Castro, Saullo G.P. (author)
For the last three decades, bistable composite laminates have gained publicity because of their outstanding features, including having two stable shapes and the ability to change these states. A common challenge regarding the analysis of these structures is the high computational cost of existing analytical methods to estimate their natural...
journal article 2022
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Knezevic, Karlo (author), Jakobović, Domagoj (author), Picek, S. (author), Ðurasević, Marko (author)
The choice of activation functions can significantly impact the performance of neural networks. Due to an ever-increasing number of new activation functions being proposed in the literature, selecting the appropriate activation function becomes even more difficult. Consequently, many researchers approach this problem from a different angle, in...
journal article 2022
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Liu, D. (author), Virgolin, Marco (author), Alderliesten, T. (author), Bosman, P.A.N. (author)
Genetic programming (GP) is one of the best approaches today to discover symbolic regression models. To find models that trade off accuracy and complexity, the non-dominated sorting genetic algorithm II (NSGA-II) is widely used. Unfortunately, it has been shown that NSGA-II can be inefficient: in early generations, low-complexity models over...
conference paper 2022
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van Ramshorst, Arjo (author)
In recent years, recommender systems have become a fundamental part of our online experience. Users rely on such systems in situations with many potential choices, such as watching a movie on a streaming service, reading a blog post, or listening to a song. Traditionally, these systems use techniques such as collaborative filtering and content...
master thesis 2021
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Mariot, L. (author), Picek, S. (author), Jakobovic, Domagoj (author), Leporati, Alberto (author)
Reversible Cellular Automata (RCA) are a particular kind of shift-invariant transformations characterized by dynamics composed only of disjoint cycles. They have many applications in the simulation of physical systems, cryptography, and reversible computing. In this work, we formulate the search of a specific class of RCA – namely, those...
journal article 2021
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Kubalík, Jiří (author), Derner, Erik (author), Babuska, R. (author)
Virtually all dynamic system control methods benefit from the availability of an accurate mathematical model of the system. This includes also methods like reinforcement learning, which can be vastly sped up and made safer by using a dynamic system model. However, obtaining a sufficient amount of informative data for constructing dynamic...
journal article 2021
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Kubalik, Jiri (author), Derner, Erik (author), Zegklitz, Jan (author), Babuska, R. (author)
Reinforcement learning algorithms can solve dynamic decision-making and optimal control problems. With continuous-valued state and input variables, reinforcement learning algorithms must rely on function approximators to represent the value function and policy mappings. Commonly used numerical approximators, such as neural networks or basis...
journal article 2021
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Verdier, C.F. (author)
Control design for modern safety-critical cyber-physical systems still requires significant expert-knowledge, since for general hybrid systems with temporal logic specifications there are no constructive methods. Nevertheless, in recent years multiple approaches have been proposed to automatically synthesize correct-by-construction controllers....
doctoral thesis 2020
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Virgolin, M. (author)
Machine learning is impacting modern society at large, thanks to its increasing potential to effciently and effectively model complex and heterogeneous phenomena. While machine learning models can achieve very accurate predictions in many applications, they are not infallible. In some cases, machine learning models can deliver unreasonable...
doctoral thesis 2020
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Derner, Erik (author), Kubalík, Jiří (author), Ancona, N. (author), Babuska, R. (author)
Developing mathematical models of dynamic systems is central to many disciplines of engineering and science. Models facilitate simulations, analysis of the system's behavior, decision making and design of automatic control algorithms. Even inherently model-free control techniques such as reinforcement learning (RL) have been shown to benefit...
journal article 2020
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Mariot, L. (author), Picek, S. (author), Jakobovic, Domagoj (author), Leporati, Alberto (author)
We consider the problem of evolving a particular kind of shift-invariant transformation – namely, Reversible Cellular Automata (RCA) defined by conserved landscape rules – using GA and GP. To this end, we employ three different optimization strategies: a single-objective approach carried out with GA and GP where only the reversibility...
conference paper 2020
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Kubalik, Jiai (author), Derner, Erik (author), Babuska, R. (author)
In symbolic regression, the search for analytic models is typically driven purely by the prediction error observed on the training data samples. However, when the data samples do not sufficiently cover the input space, the prediction error does not provide sufficient guidance toward desired models. Standard symbolic regression techniques then...
conference paper 2020
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Manzoni, Luca (author), Jakobovic, Domagoj (author), Mariot, L. (author), Picek, S. (author), Castelli, Mauro (author)
Tasks related to Natural Language Processing (NLP) have recently been the focus of a large research endeavor by the machine learning community. The increased interest in this area is mainly due to the success of deep learning methods. Genetic Programming (GP), however, was not under the spotlight with respect to NLP tasks. Here, we propose a...
conference paper 2020
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Ðurasević, M. (author), Jakobovic, Domagoj (author), Martins, Marcella Scoczynski Ribeiro (author), Picek, S. (author), Wagner, Markus (author)
Genetic programming is an often-used technique for symbolic regression: finding symbolic expressions that match data from an unknown function. To make the symbolic regression more efficient, one can also use dimensionally-aware genetic programming that constrains the physical units of the equation. Nevertheless, there is no formal analysis of...
conference paper 2020
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