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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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De Bokx, R. (author), Gravendeel, L. (author), Krause, M. (author)Bing Technology, a Philadelphia, USA based software company, seeks to develop a software framework that can be used to create forecasts for a wide range of predictive domains. In particular, they would like to create an application of this framework that is able to perform stock market forecasting. The goal of our project is to develop an...bachelor thesis 2011
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Kramer, O.J.I. (author), El Hasadi, Yousef M.F. (author), de Moel, P.J. (author), Baars, Eric T. (author), Padding, J.T. (author), van der Hoek, J.P. (author)For an accurate prediction of the porosity of a liquid-solid homogenous fluidized bed, various empirical prediction models have been developed. Symbolic regression machine learning techniques are suitable for analyzing experimental fluidization data to produce empirical expressions for porosity as a function not only of fluid velocity and...conference paper 2019
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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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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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Alibekov, Eduard (author), Kubalìk, Jiřì (author), Babuska, R. (author)This paper addresses the problem of deriving a policy from the value function in the context of reinforcement learning in continuous state and input spaces. We propose a novel method based on genetic programming to construct a symbolic function, which serves as a proxy to the value function and from which a continuous policy is derived. The...conference paper 2016
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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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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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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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Virgolin, M. (author), Alderliesten, Tanja (author), Bel, Arjan (author), Witteveen, C. (author), Bosman, P.A.N. (author)The recently introduced Gene-pool Optimal Mixing Evolutionary Algorithm for Genetic Programming (GP-GOMEA) has been shown to find much smaller solutions of equally high quality compared to other state-of-the-art GP approaches. This is an interesting aspect as small solutions better enable human interpretation. In this paper, an adaptation of...conference paper 2018
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Kubalìk, Jiřì (author), Alibekov, Eduard (author), Žegklitz, Jan (author), Babuska, R. (author)This paper presents a first step of our research on designing an effective and efficient GP-based method for symbolic regression. First, we propose three extensions of the standard Single Node GP, namely (1) a selection strategy for choosing nodes to be mutated based on depth and performance of the nodes, (2) operators for placing a compact...conference paper 2016
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Märtens, M. (author), Kuipers, F.A. (author), Van Mieghem, P.F.A. (author)Networks are continuously growing in complexity, which creates challenges for determining their most important characteristics. While analytical bounds are often too conservative, the computational effort of algorithmic approaches does not scale well with network size. This work uses Cartesian Genetic Programming for symbolic regression to...conference paper 2017
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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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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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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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Barrero, D.F. (author), Hernandez-Castro, J.C. (author), Peris-Lopez, P. (author), Camacho, D. (author), Moreno, M.D.R. (author)Radio frequency identification (RFID) is a powerful technology that enables wireless information storage and control in an economical way. These properties have generated a wide range of applications in different areas. Due to economic and technological constrains, RFID devices are seriously limited, having small or even tiny computational...journal article 2012
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Van den Bogert, H. (author), Haasnoot, E. (author), Van Kaam, N. (author), Simons, G. (author)Many problems do not have a direct solution in the form of a known algorithm or program to solve such a problem. These problems include, for example, the designing of electrical circuits and producing robots capable of locomotion. These are all part of a greater problem: the problem of synthesis. How can you make a computer design circuits and...journal article 2011
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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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Verdier, C.F. (author), Mazo, M. (author)This paper presents an automatic controller synthesis method for nonlinear systems with reachability and safety specifications. The proposed method consists of genetic programming in combination with an SMT solver, which are used to synthesize both a control Lyapunov function and the modes of a switched state feedback controller. The...journal article 2017
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