Results 21 to 30 of about 2,832,903 (299)
Dynamic environments can speed up evolution with genetic programming [PDF]
, 2011 We present a study of dynamic environments with genetic programming to ascertain if a dynamic environment can speed up evolution when compared to an equivalent static environment.
Miguel Nicolau +6 more
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Graph representations in genetic programming
, 2022 S.607-636Graph representations promise several desirable properties for genetic programming (GP); multiple-output programs, natural representations of code reuse and, in many cases, an innate mechanism for neutral drift.
Dal Piccol Sotto, Léo Francoso +4 more
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Differentiable Genetic Programming [PDF]
, 2017 We introduce the use of high order automatic differentiation, implemented via the algebra of truncated Taylor polynomials, in genetic programming. Using the Cartesian Genetic Programming encoding we obtain a high-order Taylor representation of the program output that is then used to back-propagate errors during learning.
Dario Izzo +2 more
openaire +2 more sources
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
doaj +1 more source
Open issues in genetic programming [PDF]
, 2010 It is approximately 50 years since the first computational experiments were conducted in what has become known today as the field of Genetic Programming (GP), twenty years since John Koza named and popularised the method, and ten years since the first ...
Leonardo Vanneschi +9 more
core +1 more source
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
doaj +1 more source
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
doaj +1 more source
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
openaire +2 more sources
Event-based graphical monitoring in the EpochX genetic programming framework [PDF]
, 2013 EpochX is a genetic programming framework with provision for event management – similar to the Java event model – allowing the notification of particular actions during the lifecycle of the evolutionary algorithm. It also provides a flexible Stats system
Loïc Vaseux +7 more
core +1 more source
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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