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Relevant bibliographies by topics / Genetic Programming / Dissertations / Theses

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Dissertations / Theses on the topic 'Genetic Programming'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Lones, Michael Adam. "Enzyme genetic programming : modelling biological evolvability in genetic programming." Thesis, University of York, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.399653.

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2

KOSHIYAMA, ADRIANO SOARES. "GPFIS: A GENERIC GENETIC-FUZZY SYSTEM BASED ON GENETIC PROGRAMMING." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2014. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=26560@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>PROGRAMA DE EXCELENCIA ACADEMICA<br>Sistemas Fuzzy-Genéticos compreendem uma área que une Sistemas de Inferência Fuzzy e Meta-Heurísticas prevalentes nos conceitos de seleção natural e recombinação genética. Esta é de grande interesse para a comunidade científica, pois propicia a descoberta de conhecimento em áreas onde a compreensão do fenômeno em estudo é exíguo, além de servir de apoio à decisão para gestores público-privados. O objetivo desta dissertação é desenvolver um no

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3

Martin, Peter N. "Genetic programming in hardware." Thesis, University of Essex, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.272585.

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4

Cavill, Rachel. "Multi-chromosomal genetic programming." Thesis, University of York, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.437617.

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5

Suleman, Hussein. "Genetic Programming in Mathematica." Thesis, University of Durban-Westville, 1997. http://hdl.handle.net/10919/71533.

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GP has traditionally been implemented in LISP but there is a slow migration towards faster languages like C++. Any implementation language is dictated not only by the speed of the platform but also by the desirability of such an implementation. With a large number of scientists migrating to scientifically-biased programming languages like Mathematica, such provides an ideal testbed for GP.In this study it was attempted to implement GP on a Mathematica platform, exploiting the advantages of Mathematica's unique capabilities. Wherever possible, optimizations have been applied to drive the GP alg

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6

Steinhoff, Robert J. "A Performance Comparison of Tree-Based Genetic Programming versus Stack-Based Genetic Programming versus Stack-Based Genetic Programming Using the Java Virtual Machine." NSUWorks, 2000. http://nsuworks.nova.edu/gscis_etd/859.

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Stack-based genetic programming uses variable length, linear programs executing on a virtual stack machine. This concept was proposed and evaluated by Timothy Perkis. The Java programming language uses a stack-based virtual machine to perform operations. This paper examined the possibility of performing stack-based genetic programming directly using the stack on the Java virtual machine. The problems of combining stack based genetic programming with the Java virtual machine stack were explored. Excessive runtime delay on bytecode verification of the chromosome carrying classes undergoing fitne

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7

Smith, Steven Lee Al-Mahmood Mohammed Abdul latif. "Knowledge discovery using genetic programming /." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1993. http://handle.dtic.mil/100.2/ADA276224.

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Thesis (M.S. in Information Technology Management) Naval Postgraduate School, December 1993.<br>Thesis advisor(s): B. Ramesh ; William R. Gates. "December 1993." Includes bibliographical references. Also available online.

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8

Johansson, Magnus. "Financial application of genetic programming." Thesis, Linköping University, Department of Computer and Information Science, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-17091.

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9

Norin, Martin. "Automatic Modularization in Genetic Programming." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-150822.

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This master’s thesis is an investigation into automatically created functions when applying Genetic Programming to a problem. The thesis begins with a description of Genetic Programming and the different parts that is required to apply Genetic Programming to a problem. This is followed by descriptions of different methods for automatically creating functions when using Genetic Programming as a problem solving method. A new method for automatically creating functions is proposed with the name Structural Module Creation. Structural Module Creation creates new functions based on the most frequent

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10

Smith, Steven Lee, and Mohammed Abdul latif Al-Mahmood. "Knowledge discovery using genetic programming." Thesis, Monterey, California. Naval Postgraduate School, 1993. http://hdl.handle.net/10945/26608.

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Dramatic growth in database technology has outpaced the ability to analyze the information stored in databases for new knowledge and has created an increasing potential for the loss of undiscovered knowledge. This potential gains for such knowledge discovery are particularly large in the Department of Defense where millions of transactions, from maintenance to medical information, are recorded yearly. Due to the limitations of traditional knowledge discovery methods in analyzing this data, there is a growing need to utilize new knowledge discovery methods to glean knowledge from vast databases

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11

Hinchliffe, Mark. "Dynamic modelling using genetic programming." Thesis, University of Newcastle Upon Tyne, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.391407.

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12

Searson, Dominic Patrick. "Nonlinear PLS using genetic programming." Thesis, University of Newcastle Upon Tyne, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.246644.

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13

Zheng, QiZhi. "Genetic programming based structure optimisation." Thesis, University of Leeds, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.417724.

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14

Dimopoulos, Christos. "Genetic programming for manufacturing optimisation." Thesis, University of Sheffield, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.327668.

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15

Deakin, Anthony Grayham. "Evolving strategies with genetic programming." Thesis, University of Liverpool, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.272651.

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16

Kuscu, Ibrahim. "Evolutionary generalisation and genetic programming." Thesis, University of Sussex, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.285062.

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17

DIAS, DOUGLAS MOTA. "QUANTUM-INSPIRED LINEAR GENETIC PROGRAMMING." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2010. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=17544@1.

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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>A superioridade de desempenho dos algoritmos quânticos, em alguns problemas específicos, reside no uso direto de fenômenos da mecânica quântica para realizar operações com dados em computadores quânticos. Esta característica fez surgir uma nova abordagem, denominada Computação com Inspiração Quântica, cujo objetivo é criar algoritmos clássicos (executados em computadores clássicos) que tirem proveito de princípios da mecânica quântica para melhorar seu desempenho. Neste sentido, alguns algoritmos evolutivos com inspiração quântica

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18

Fine, Steven B. "Extensions to behavioral genetic programming." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/112846.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (page 55).<br>In this work I introduce genetic programming [5] as a general technique to produce programs with arbitrary behavior. I discuss genetic programming and its application the task of symbolic regression. I introduce behav

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19

O'Reilly, Una-May Carleton University Dissertation Computer Science. "An analysis of genetic programming." Ottawa, 1995.

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20

Caroli, Alberto. "Genetic Programming applications in Robotics." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2013. http://amslaurea.unibo.it/5860/.

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21

Preo, Alice <1990&gt. "Option Pricing with Genetic Programming." Master's Degree Thesis, Università Ca' Foscari Venezia, 2015. http://hdl.handle.net/10579/5981.

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In the last decades, an increasing attention has been directed to the application of Genetic Programming. This approach has been adopted to a wide number of fields, reporting successful results also in finance domain as automated computational programming tool. Starting from the theoretical research in Genetic Programming, the aim of this work is to focus on the actual implementation of this methodology to the financial field, especially on the prediction of derivative securities behavior. The attention will be centered on the option pricing and empirical tests will be carried out on market da

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22

Innes, Andrew, and andrew innes@defence gov au. "Genetic Programming for Cephalometric Landmark Detection." RMIT University. Aerospace, Mechanical and Manufacturing Engineering, 2007. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080221.123310.

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The domain of medical imaging analysis has burgeoned in recent years due to the availability and affordability of digital radiographic imaging equipment and associated algorithms and, as such, there has been significant activity in the automation of the medical diagnostic process. One such process, cephalometric analysis, is manually intensive and it can take an experienced orthodontist thirty minutes to analyse one radiology image. This thesis describes an approach, based on genetic programming, neural networks and machine learning, to automate this process. A cephalometric analysis involves

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23

Oumanski, Alexandre. "Object-oriented approach to genetic programming." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ39116.pdf.

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24

Brameier, Markus [Verfasser]. "On linear genetic programming / Markus Brameier." Dortmund : Universitätsbibliothek Technische Universität Dortmund, 2005. http://d-nb.info/1011533146/34.

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25

Rodriguez, Adelein. "A NEAT Approach to Genetic Programming." Master's thesis, University of Central Florida, 2007. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2379.

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The evolution of explicitly represented topologies such as graphs involves devising methods for mutating, comparing and combining structures in meaningful ways and identifying and maintaining the necessary topological diversity. Research has been conducted in the area of the evolution of trees in genetic programming and of neural networks and some of these problems have been addressed independently by the different research communities. In the domain of neural networks, NEAT (Neuroevolution of Augmenting Topologies) has shown to be a successful method for evolving increasingly complex networks

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26

Char, Kalyani Govinda. "Constructivist artificial intelligence with genetic programming." Thesis, University of Glasgow, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.265641.

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27

Hiden, Hugo George. "Data-based modelling using genetic programming." Thesis, University of Newcastle Upon Tyne, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.246137.

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28

Ahluwalia, Manu. "Co-evolving functions in genetic programming." Thesis, University of the West of England, Bristol, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.322427.

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29

SILVA, CLEOMAR PEREIRA DA. "MASSIVELY PARALLEL GENETIC PROGRAMMING ON GPUS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2014. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=24129@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO<br>PROGRAMA DE EXCELENCIA ACADEMICA<br>A Programação Genética permite que computadores resolvam problemas automaticamente, sem que eles tenham sido programados para tal. Utilizando a inspiração no princípio da seleção natural de Darwin, uma população de programas, ou indivíduos, é mantida, modificada baseada em variação genética, e avaliada de acordo com uma função de aptidão (fitness). A programação genética tem sid

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30

PRAXEDES, ERIC DA SILVA. "QUANTITATIVE SEISMIC INTERPRETATION USING GENETIC PROGRAMMING." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2014. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=24789@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>PROGRAMA DE EXCELENCIA ACADEMICA<br>Uma das tarefas mais importantes na indústria de exploração e produção de petróleo é a discriminação litológica. Uma das principais fontes de informação para subsidiar a discriminação e caracterização litológica é a perfilagem que é corrida no poço. Porém, na grande maioria dos trabalhos os perfis utilizados na discriminação litológica são apenas aqueles disponíveis no domínio dos poços. Para que modelos de discriminação litológica possam ser

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31

Yu, Chris 1981. "Characterizing function inlining with genetic programming." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/33392.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.<br>Includes bibliographical references (leaves 74-75).<br>Function inlining is a compiler optimization where the function call is replaced by the code from the function itself. Using a form of machine learning called genetic programming, this thesis examines which factors are important in determining which function calls to inline to maximize performance. A number of different heuristics are generated for inlining decisions in the Trimaran compiler, which improve on performance

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32

König, Rikard. "Enhancing genetic programming for predictive modeling." Doctoral thesis, Högskolan i Borås, Institutionen Handels- och IT-högskolan, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-3689.

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<p>Avhandling för teknologie doktorsexamen i datavetenskap, som kommer att försvaras offentligt tisdagen den 11 mars 2014 kl. 13.15, M404, Högskolan i Borås. Opponent: docent Niklas Lavesson, Blekinge Tekniska Högskola, Karlskrona.</p>

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33

Bearpark, Keith. "Learning and memory in genetic programming." Thesis, University of Southampton, 2000. https://eprints.soton.ac.uk/45930/.

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Genetic Programming is a form of Evolutionary Computation in which computer programs are evolved by methods based on simulating the natural evolution of biological species. A new generation of a species acquires the characteristics of previous generations through the inheritance of genes by sexual reproduction and through random changes in alleles by random mutation. The new generation may enhance its ability to survive by the acquisition of cultural knowledge through learning processes. This thesis combines the transfer of knowledge by genetic means with the transfer of knowledge by cultural

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34

Paterson, Norman R. "Genetic programming with context-sensitive grammars." Thesis, University of St Andrews, 2003. http://hdl.handle.net/10023/14984.

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This thesis presents Genetic Algorithm for Deriving Software (Gads), a new technique for genetic programming. Gads combines a conventional genetic algorithm with a context-sensitive grammar. The key to Gads is the onto genic mapping, which converts a genome from an array of integers to a correctly typed program in the phenotype language defined by the grammar. A new type of grammar, the reflective attribute grammar (rag), is introduced. The rag is an extension of the conventional attribute grammar, which is designed to produce valid sentences, not to recognize or parse them. Together, Gads and

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35

White, David Robert. "Genetic programming for low-resource systems." Thesis, University of York, 2009. http://etheses.whiterose.ac.uk/757/.

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Embedded systems dominate the computing landscape. This dominance is increasing with the advent of ubiquitous computing whereby lightweight, low-resource systems are being deployed on a vast scale. These systems present new engineering challenges: high-volume production places a stronger emphasis on absolute cost, resources available to executing software are highly constrained, and physical manufacturing capabilities approach hard limits. Add to this the sensitive nature of many of these systems, such as smartcards used for financial transactions, and the development of these systems becomes

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36

De, Lorenzo Andrea. "Genetic Programming Techniques in Engineering Applications." Doctoral thesis, Università degli studi di Trieste, 2014. http://hdl.handle.net/10077/9991.

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2012/2013<br>Machine learning is a suite of techniques that allow developing algorithms for performing tasks by generalizing from examples. Machine learning systems, thus, may automatically synthesize programs from data. This approach is often feasible and cost-effective where manual programming or manual algorithm design is not. In the last decade techniques based on machine learning have spread in a broad range of application domains. In this thesis, we will present several novel applications of a specific machine Learning technique, called Genetic Programming, to a wide set of enginee

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37

Kishore, Krishna J. "Genetic Programming Based Multicategory Pattern Classification." Thesis, Indian Institute of Science, 2001. https://etd.iisc.ac.in/handle/2005/75.

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Nature has created complex biological structures that exhibit intelligent behaviour through an evolutionary process. Thus, intelligence and evolution are intimately connected. This has inspired evolutionary computation (EC) that simulates the evolutionary process to develop powerful techniques such as genetic algorithms (GAs), genetic programming (GP), evolutionary strategies (ES) and evolutionary programming (EP) to solve real-world problems in learning, control, optimization and classification. GP discovers the relationship among data and expresses it as a LISP-S expression i.e., a computer

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Kishore, Krishna J. "Genetic Programming Based Multicategory Pattern Classification." Thesis, Indian Institute of Science, 2001. http://hdl.handle.net/2005/75.

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Nature has created complex biological structures that exhibit intelligent behaviour through an evolutionary process. Thus, intelligence and evolution are intimately connected. This has inspired evolutionary computation (EC) that simulates the evolutionary process to develop powerful techniques such as genetic algorithms (GAs), genetic programming (GP), evolutionary strategies (ES) and evolutionary programming (EP) to solve real-world problems in learning, control, optimization and classification. GP discovers the relationship among data and expresses it as a LISP-S expression i.e., a computer

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39

Abrahamsen, Christian Topp. "A genetic approach to prenatal glucocorticoid programming." Thesis, University of Edinburgh, 2007. http://hdl.handle.net/1842/24708.

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11β-hydroxysteroid dehydrogenase type 2 (11β-HSD2) rapidly inactivates GCs within the placenta and other fetal tissues, hence serving as a ‘barrier’ to limit exposure of the fetus to maternal GCs, ensuring an appropriate fetal milieu for normal development. The synthetic GC, dexamethasone (Dex) is thought to bypass the 11β-HSD2 ‘barrier’ due to poor substrate specificity. Prenatal administration in rats has been shown to programme low birth weight and anxiety, associated with hypothalamic pituitary adrenal (HPA) axis hyperactivity and the decreased hippocampal gene expression of corticosteroid

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40

August, Riley. "Applying genetic programming to scripted mobile robotics." Thesis, University of Ottawa (Canada), 2009. http://hdl.handle.net/10393/28474.

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In this thesis, we develop a new language for genetic programming, specifically designed for high-level controller scripting on mobile robots. We then test this language against previous conventions on the Robots Everywhere Antbot platform. We develop a genetic programming framework using Python and the new language, to create corridor-following programs in a simple simulation. Using this framework, we conduct a variety of experiments to find good parameters and techniques that apply to this new language, which can evolve the best controllers. Our results suggest that hierarchical GP using a m

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41

Li, Xiang, and xiali@cs rmit edu au. "Utilising Restricted For-Loops in Genetic Programming." RMIT University. Computer Science and Information Technology, 2007. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080110.122751.

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Genetic programming is an approach that utilises the power of evolution to allow computers to evolve programs. While loops are natural components of most programming languages and appear in every reasonably-sized application, they are rarely used in genetic programming. The work is to investigate a number of restricted looping constructs to determine whether any significant benefits can be obtained in genetic programming. Possible benefits include: Solving problems which cannot be solved without loops, evolving smaller sized solutions which can be more easily understood by human programmers

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42

Lotz, Marco. "Modelling of process systems with Genetic Programming /." Thesis, Link to the online version, 2006. http://hdl.handle.net/10019/570.

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Preen, Richard John. "Dynamical genetic programming in learning classifier systems." Thesis, University of the West of England, Bristol, 2011. http://eprints.uwe.ac.uk/25852/.

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Learning Classifier Systems (LCS) traditionally use a ternary encoding to generalise over the environmental inputs and to associate appropriate actions. However, a number of schemes have been presented beyond this, ranging from integers to artificial neural networks. This thesis investigates the use of Dynamical Genetic Programming (DGP) as a knowledge representation within LCS. DGP is a temporally dynamic, graph-based, symbolic representation. Temporal dynamism has been identified as an important aspect in biological systems, artificial life, and cognition in general. Furthermore, discrete dy

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Gustafson, Steven Matt. "An analysis of diversity in genetic programming." Thesis, University of Nottingham, 2004. http://eprints.nottingham.ac.uk/10057/.

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Genetic programming is a metaheuristic search method that uses a population of variable-length computer programs and a search strategy based on biological evolution. The idea of automatic programming has long been a goal of artificial intelligence, and genetic programming presents an intuitive method for automatically evolving programs. However, this method is not without some potential drawbacks. Search using procedural representations can be complex and inefficient. In addition, variable sized solutions can become unnecessarily large and difficult to interpret. The goal of this thesis is to

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45

Esparcia, Alcázar Anna Isabel. "Genetic programming for adaptive digital signal processing." Thesis, University of Glasgow, 1998. http://theses.gla.ac.uk/4780/.

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GUILLEN, JEFF MAYNARD. "STUDIES ON RESERVOIR CHARACTERIZATION VIA GENETIC PROGRAMMING." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2015. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=25771@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO<br>PROGRAMA DE EXCELENCIA ACADEMICA<br>Na área de exploração e produção de petróleo são alocados grandes investimentos para conseguir diminuir os riscos associados à baixos níveis de produção, que podem ser minimizados mediante a acertada caracterização do reservatório de petróleo. Uma valiosa fonte de informação pode ser extraída de dados sísmicos 3D, obtidos do campo em estudo. O custo econômico de aquisição de est

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47

Al-Madi, Naila Shikri. "Improved Genetic Programming Techniques For Data Classification." Diss., North Dakota State University, 2014. https://hdl.handle.net/10365/27097.

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Evolutionary algorithms are one category of optimization techniques that are inspired by processes of biological evolution. Evolutionary computation is applied to many domains and one of the most important is data mining. Data mining is a relatively broad field that deals with the automatic knowledge discovery from databases and it is one of the most developed fields in the area of artificial intelligence. Classification is a data mining method that assigns items in a collection to target classes with the goal to accurately predict the target class for each item in the data. Genetic programmin

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Chitty, Darren M. "Improving the computational speed of genetic programming." Thesis, University of Bristol, 2015. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.686812.

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Genetic Programming (GP) is well known as a computationally intensive technique especially when considering regression or classification tasks with large datasets. Consequently, there has been considerable work conducted into improving the computational speed of GP. Recently, this has concentrated on exploiting highly parallel architectures in the form of Graphics Processing Units (GPUs). However, the reported speeds fall considerably short of the computational capabilities of these GPUs. This thesis investigates this issue, seeking to considerably improve the computational speed of GP. Indeed

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Beadle, Lawrence. "Semantic and structural analysis of genetic programming." Thesis, University of Kent, 2009. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.509628.

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Genetic programming (GP) is a subset of evolutionary computation where candidate solutions are evaluated through execution or interpreted execution. The candidate solutions generated by GP are in the form of computer programs, which are evolved to achieve a stated objective. Darwinian evolutionary theory inspires the processes that make up GP which include crossover, mutation and selection. During a GP run, crossover, mutation and selection are performed iteratively until a program that satisfies the stated objectives is produced or a certain number of time steps have elapsed. The objectives o

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CASTELLI, MAURO. "Measures and methods for robust genetic programming." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2012. http://hdl.handle.net/10281/28571.

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This thesis proposes several measures for a better understanding of the dynamic of the evolutionary process. In particular, measures to control bloat, overfitting and complexity of the solutions are defined. Moreover, several methods to improve the generalization ability of the solutions produced by the standard Genetic Programming (GP) algorithm are proposed. This thesis also focuses on the role of semantic in GP. In particular, a new GP algorithm that uses only semantic information is proposed. Finally, a study on the effect of multi objective optimization in GP is performed.

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