Genetic Programming for System Identification

2011 ◽  
pp. 135-168 ◽  
Author(s):  
Lavinia Ferariu ◽  
Alina Patelli
Keyword(s):  
Genetic Programming ◽  
Mathematical Description ◽  
Nonlinear Models ◽  
Theoretical Background ◽  
Industrial Plant ◽  
Valuable Alternative ◽  
Design Scheme ◽  
Simulated System ◽  
Multiobjective Algorithms ◽  
Selection Of

This chapter discusses the features of genetic programming based identification approaches, starting with the connected theoretical background. The presentation reveals both advantages and limitations of the methodology and offers several recommendations useful for making GP techniques a valuable alternative for mathematical models’ construction. For a sound illustration of the discussed design scheme, two GP-based multiobjective algorithms are suggested. They permit a flexible selection of nonlinear models, linear in parameters, by advantageously exploiting their particular structure, thus improving the exploration capabilities of GP and the interpretability of the resulted mathematical description. Both model accuracy and parsimony are addressed, by means of non-elitist and elitist Pareto techniques, aimed at adapting the priority of each involved objective. The algorithms’ performances are illustrated on two applications of different complexity levels, namely the identification of a simulated system, and the identification of an industrial plant.

General Schema Theory for Genetic Programming with Subtree-Swapping Crossover: Part II

2003 ◽  
Vol 11 (2) ◽  
pp. 169-206 ◽  
Author(s):  
Riccardo Poli ◽  
Nicholas Freitag McPhee
Keyword(s):  
Genetic Programming ◽  
Schema Theory ◽  
Exact Formulation ◽  
Expected Number ◽  
Size Evolution ◽  
General Schema ◽  
Definition Of ◽  
Effective Fitness ◽  
Selection Of ◽  
Exact Definition

This paper is the second part of a two-part paper which introduces a general schema theory for genetic programming (GP) with subtree-swapping crossover (Part I (Poli and McPhee, 2003)). Like other recent GP schema theory results, the theory gives an exact formulation (rather than a lower bound) for the expected number of instances of a schema at the next generation. The theory is based on a Cartesian node reference system, introduced in Part I, and on the notion of a variable-arity hyperschema, introduced here, which generalises previous definitions of a schema. The theory includes two main theorems describing the propagation of GP schemata: a microscopic and a macroscopic schema theorem. The microscopic version is applicable to crossover operators which replace a subtree in one parent with a subtree from the other parent to produce the offspring. Therefore, this theorem is applicable to Koza's GP crossover with and without uniform selection of the crossover points, as well as one-point crossover, size-fair crossover, strongly-typed GP crossover, context-preserving crossover and many others. The macroscopic version is applicable to crossover operators in which the probability of selecting any two crossover points in the parents depends only on the parents' size and shape. In the paper we provide examples, we show how the theory can be specialised to specific crossover operators and we illustrate how it can be used to derive other general results. These include an exact definition of effective fitness and a size-evolution equation for GP with subtree-swapping crossover.

Thermoeconomic Model for the Energy Optimization of Industrial Air Compressed Supply Network Under Transient Condition

2012 ◽  
Author(s):  
Lesme Corredor M. ◽  
Diego Guillen ◽  
José Prada ◽  
Alisson Contreras
Keyword(s):  
Energy Demand ◽  
Energy Optimization ◽  
Industrial Plant ◽  
Transient Condition ◽  
Power Demand ◽  
Flow Monitoring ◽  
Air Supply ◽  
Air Compressors ◽  
Air Compression ◽  
Selection Of

Air compression represents around 20% of industrial total electric power demand, especially in chemicals and process companies. Few technical studies related with energy optimization of air compressed networks are reported in the specialized literature, in contrast, in natural gas and steam networks have been widely analyzed. Pressure, temperature and flow monitoring of air compression is not enough for implementation of energy optimization models, for this reason authors have developed a transit conditions model which takes into account air supply equipments and air compressed process requirements. This paper presents a decision support system for the scheduling selection of a set of air compressors in an industrial plant based on energy demand minimization. Several constraints must be taken in consideration during the optimization process, this can be desegregate in two types, the first set of constrains was used for simulate the operation of scroll, screw and centrifuges compressors, the second based in graph an node theory and contain the mathematical transit conditions model of supply air network topology, for the complexity of the problem the use of a genetic algorithm to search an optimal combination was necessary.

Machine Fault Detection Using Genetic Programming

2005 ◽  
Author(s):  
B. Samanta
Keyword(s):  
Fault Detection ◽  
Genetic Programming ◽  
Vibration Data ◽  
Rotating Machine ◽  
Fault Recognition ◽  
Machine Fault ◽  
Artificial Neural Network Ann ◽  
Monitoring And Diagnostics ◽  
The Time Domain ◽  
Selection Of

Applications of genetic programming (GP) include many areas. However applications of GP in the area of machine condition monitoring and diagnostics is very recent and yet to be fully exploited. In this paper, a study is presented to show the performance of machine fault detection using GP. The time domain vibration signals of a rotating machine with normal and defective gears are processed for feature extraction. The extracted features from original and preprocessed signals are used as inputs to GP for two class (normal or fault) recognition. The number of features and the features are automatically selected in GP maximizing the classification success. The results of fault detection are compared with genetic algorithm (GA) based artificial neural network (ANN)- termed here as GA-ANN. The number of hidden nodes in the ANN and the selection of input features are optimized using GAs. Two different normalization schemes for the features have been used. For each trial, the GP and GA-ANN are trained with a subset of the experimental data for known machine conditions. The trained GP and GA-ANN are tested using the remaining set of data. The procedure is illustrated using the experimental vibration data of a gearbox. The results compare the effectiveness of both types of classifiers with GP and GA based selection of features.

Recollimation shocks in relativistic jets

2018 ◽  
Vol 27 (10) ◽  
pp. 1844011 ◽  
Author(s):  
José M. Martí ◽  
Manel Perucho ◽  
José L. Gómez ◽  
Antonio Fuentes
Keyword(s):  
Magnetic Fields ◽  
Numerical Simulations ◽  
Theoretical Background ◽  
Synchrotron Emission ◽  
Relativistic Jets ◽  
Dominant Type ◽  
X Ray ◽  
Extragalactic Jets ◽  
X Ray Binaries ◽  
Selection Of

Recollimation shocks (RS) appear associated with relativistic flows propagating through pressure mismatched atmospheres. Astrophysical scenarios invoking the presence of such shocks include jets from AGNs and X-ray binaries and GRBs. We shall start reviewing the theoretical background behind the structure of RS in overpressured jets. Next, basing on numerical simulations, we will focus on the properties of RS in relativistic steady jets threaded by helical magnetic fields depending on the dominant type of energy. Synthetic radio maps from the simulation of the synchrotron emission for a selection of models in the context of parsec-scale extragalactic jets will also be discussed.

Mentoring for Novice Teachers

2018 ◽  
pp. 143-164
Author(s):  
Hurriyet Alatas
Keyword(s):  
Novice Teachers ◽  
Theoretical Background ◽  
Mentor Teachers ◽  
Mentor Training ◽  
Roles And Responsibilities ◽  
Mentoring Practices ◽  
Selection Of

The purpose of this chapter is to explicate the concept of “mentoring.” First, the confusion on the “mentoring” concept is fixed in this chapter and then the purposes of mentoring practices are examined. The theoretical background of mentoring, roles and responsibilities of mentors, and main mentoring models are reviewed. Then, benefits of mentoring practices are discussed thoroughly. After clearing up the concept of “mentoring,” the qualities that a mentor should have are mentioned in this chapter. Additionally, the selection of mentors, mentor-novice matching, and mentor training issues are highlighted. Finally, the interaction between mentors and novice teachers, the effect of mentors' and novice teachers' workload to mentoring practices, and incentives for mentor teachers are discussed.

scholarly journals Selection of Multimode Resource-Constrained Project Scheduling Scheme Based on DEA Method

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Song Zhang
Keyword(s):  
Project Scheduling ◽  
Data Envelopment ◽  
Resource Constrained ◽  
Resource Constrained Project Scheduling ◽  
Scheduling Scheme ◽  
Solution Selection ◽  
Project Scheduling Problem ◽  
Dea Method ◽  
Multiobjective Algorithms ◽  
Selection Of

A multimode resource-constrained project scheduling problem (MRCPSP) may have multifeasible solutions, due to its nature of targeting multiobjectives. Given the NP-hard MRCPSP and intricate multiobjective algorithms, finding the optimized result among those solutions seems impossible. This paper adopts data envelopment analysis (DEA) to evaluate a series of solutions of an MRCPSP and to find an appropriate choice in an objective way. Our approach is applied to a typical MRCPSP in practice, and the results validate that DEA is an effective and objective method for MRCPSP solution selection.

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