Genetic Programming-Based Machine Degradation Modeling Methodology
IEEE Open Journal of Instrumentation and Measurement, 2022 Machine degradation is a complex, dynamic and irreversible process and its modeling is a leading-edge technology in prognostics and health management (PHM).
Tongtong Yan, Dong Wang
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Bias-variance decomposition in Genetic Programming
Open Mathematics, 2016 We study properties of Linear Genetic Programming (LGP) through several regression and classification benchmarks. In each problem, we decompose the results into bias and variance components, and explore the effect of varying certain key parameters on the
Kowaliw Taras, Doursat René
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Forecasting Shaharchay River Flow in Lake Urmia Basin using Genetic Programming and M5 Model Tree
مجله آب و خاک, 2017 Introduction: Precise prediction of river flows is the key factor for proper planning and management of water resources. Thus, obtaining the reliable methods for predicting river flows has great importance in water resource engineering.
S. Samadianfard, R. Delirhasannia
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The Train Delay Model Developed by the Genetic Programming Algorithm
Journal of Advanced Transportation, 2022 The paper discusses the problem of probability distribution category identification of train delay data by a genetic programming algorithm. This train delay frequency function and the probability distribution simply derived from it are significant to ...
Tomas Brandejsky
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A Comparison of Cartesian Genetic Programming and Linear Genetic Programming [PDF]
, 2008 Two prominent genetic programming approaches are the graph-based Cartesian Genetic Programming (CGP) and Linear Genetic Programming (LGP). Recently, a formal algorithm for constructing a directed acyclic graph (DAG) from a classical LGP instruction sequence has been established.
Garnett Carl Wilson, Wolfgang Banzhaf
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EpochX: Genetic Programming in Java with Statistics and Event Monitoring [PDF]
, 2012 EpochX is a Genetic Programming (GP) framework written in Java. It allows the creation of tree-based and grammar-based GP systems. It has been created to reflect typical ways in which Java programmers work, for example, borrowing from the Java event ...
Colin Johnson +5 more
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Evolutionary Algorithms in a Bacterial Consortium of Synthetic Bacteria
Algorithms, 2023 At present, synthetic biology applications are based on the programming of synthetic bacteria with custom-designed genetic circuits through the application of a top-down strategy. These genetic circuits are the programs that implement a certain algorithm,
Sara Lledó Villaescusa +1 more
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NNC: A tool based on Grammatical Evolution for data classification and differential equation solving
SoftwareX, 2019 A genetic programming tool is demonstrated for data classification and differential equation solving. The fundamental element of the method is the well-known technique of Grammatical Evolution.
Ioannis G. Tsoulos +2 more
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On Comprehension of Genetic Programming Solutions: A Controlled Experiment on Semantic Inference
Mathematics, 2022 Applied to the problem of automatic program generation, Genetic Programming often produces code bloat, or unexpected solutions that are, according to common belief, difficult to comprehend.
Boštjan Slivnik +3 more
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Automated Problem Decomposition for the Boolean Domain with Genetic Programming [PDF]
, 2013 Researchers have been interested in exploring the regularities and modularity of the problem space in genetic programming (GP) with the aim of decomposing the original problem into several smaller subproblems.
Otero, Fernando E.B. +3 more
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