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Evolving artificial terrains with automated genetic terrain programing

Frade, Miguel
Fonte: Instituto Politécnico de Leiria Publicador: Instituto Politécnico de Leiria
Tipo: Tese de Doutorado
Publicado em 23/11/2012 ENG
Relevância na Pesquisa
45.95%
Tese de Doutoramento apresentada à Universidad de Extremadura.; Nowadays video game industry is facing a big challenge: keep costs under control as games become bigger and more complex. Creation of game content, such as character models, maps, levels, textures, sound effects and so on, represent a big slice of total game production cost. Hence, video game industry is increasingly turning to procedural content generation to amplify the cost-effectiveness of the efforts of video game designers. However, creating and fine tunning procedural methods for automated content generation is a time consuming task. In this thesis we detail a Genetic Programming based procedural content technique to generate procedural terrains. Those terrains present aesthetic appeal and do not require any parametrization to control its look. Thus, allowing to save time and help reducing production costs. To accomplish these features we devised the Genetic Terrain Programming (GTP) technique. The first implementation of GTP used an Interactive Evolutionary Computation (IEC) approach, were a user guides the evolutionary process. In spite of the good results achieved this way, this approach was limited by user fatigue (a common trait of IEC systems). To address this issue a second version of GTP was developed where the search is automated...

Reconhecimento semi-automatico e vetorização de regiões em imagens de sensoriamento remoto; Semi-automatic recognition and vectorization of regions in remote sensig images

Jefersson Alex dos Santos
Fonte: Biblioteca Digital da Unicamp Publicador: Biblioteca Digital da Unicamp
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 27/02/2009 PT
Relevância na Pesquisa
55.89%
O uso de imagens de sensoriamento remoto (ISRs) como fonte de informação em aplicações voltadas para o agro-negócio e bastante comum. Nessas aplicações, saber como é a ocupação espacial é fundamental. Entretanto, reconhecer e diferenciar regiões de culturas agrícolas em ISRs ainda não é uma tarefa trivial. Embora existam métodos automáticos propostos para isso, os usuários preferem muitas vezes fazer o reconhecimento manualmente. Isso acontece porque tais métodos normalmente são feitos para resolver problemas específicos, ou quando são de propósito geral, não produzem resultados satisfatórios fazendo com que, invariavelmente, o usuário tenha que revisar os resultados manualmente. A pesquisa realizada objetivou a especificação e implementação parcial de um sistema para o reconhecimento semi-automático e vetorização de regiões em imagens de sensoriamento remoto. Para isso, foi usada uma estratégia interativa, chamada realimentação de relevância, que se baseia no fato de o sistema de classificação poder aprender quais são as regiões de interesse utilizando indicações de relevância feitas pelo usuário do sistema ao longo de iterações. A idéia é utilizar descritores de imagens para codificar informações espectrais e de textura de partições das imagens e utilizar realimentação de relevância com Programação Genética (PG) para combinar as características dos descritores. PG é uma técnica de aprendizado de máquina baseada na teoria da evolução. As principais contribuições deste trabalho são: estudo comparativo de técnicas de vetorização de imagens; adaptação do modelo de recuperação de imagens por conteúdo proposto recentemente para realização de realimentação de relevância usando regiões de imagem; adaptação do modelo de realimentação de relevância para o reconhecimento de regiões em ISRs; implementação parcial de um sistema de reconhecimento semi-automático e vetorização de regiões em ISRs; proposta de metodologia de validação do sistema desenvolvido; The use of remote sensing images as a source of information in agrobusiness applications is very common. In these applications...

Uso de técnicas de aprendizagem para classificação e recuperação de imagens; Use of learning techniques for image classification and retrieval

Fábio Augusto Faria
Fonte: Biblioteca Digital da Unicamp Publicador: Biblioteca Digital da Unicamp
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 19/03/2010 PT
Relevância na Pesquisa
55.89%
Técnicas de aprendizagem vêm sendo empregadas em diversas áreas de aplicação (medicina, biologia, segurança, entre outras). Neste trabalho, buscou-se avaliar o uso da técnica de Programação Genética (PG) em tarefas de recuperação e classificação de imagens. PG busca soluções ótimas inspirada pela teoria de seleção natural das espécies. Indivíduos mais aptos (melhores soluções) tendem a evoluir e se reproduzir nas gerações futuras. As principais contribuições deste trabalho são: implementação de um classificador de imagens utilizando PG para combinar evidencias visuais (descritores de imagens) e assim, obter melhores resultados com relação à eficácia de classificação; Comparação de PG com outras técnicas de aprendizagem em tarefas de recuperação de imagens por conteúdo; Uso de regras de associação para recuperação de imagens; Learning techniques have been used in several applications (medicine, biology, surveillance systems, e.g.) This work aims to evaluate the use of the Genetic Programming (GP) learning technique for image retrieval and classification tasks. This technique is a problem-solving system that follows principles of inheritance and evolution, inspired by the idea of Natural Selection. The space of all possible solutions is investigated using a set of optimization techniques that imitate the theory of evolution. The main contributions of this work are: proposal of classifier implementation using GP to combine visual evidences (image descriptors) to be used in image classification tasks; comparison of GP with other learning techniques in content-based image retrieval tasks

Complexity Drift in Evolutionary Computation with Tree Representations

Rosca, Justinian P. ; Ballard, Dana Harry
Fonte: University of Rochester. National Resource Laboratory for the Study of Brain and Behavior. Publicador: University of Rochester. National Resource Laboratory for the Study of Brain and Behavior.
Tipo: Relatório
ENG
Relevância na Pesquisa
55.93%
One serious problem of standard Genetic Programming (GP) is that evolved expressions appear to drift towards large and slow forms on average. This report presents a novel analysis of the role played by variable complexity in the selection and survival of GP expressions. It defines a particular property of GP representations, called rooted tree-schema, that sheds light on the role of variable complexity of evolved representations. A tree-schema is a relation on the space of tree-shaped structures which provides a quantifiable partitioning of the search space. The present analysis answers questions such as: What role does variable complexity play in the selection and survival of evolved expressions? What is the influence of a parsimony penalty? How heavy should parsimony penalty be weighted or how should it be adapted in order to preserve the underlying optimization process? Are there alternative approaches to simulating a parsimony penalty that do not result in a change of the fitness landscape? The present report provides theoretical answers to these questions, interpretation of these results, and an experimental perspective.

Programação Genética Aplicada no Processo de Descoberta de Conhecimento em Bases de Dados de Redes de Pesquisa.; Genetic Programming Apllied in the Process of Knowledge Discovery in Databases for Research Networks.

DUARTE, Kedma Batista
Fonte: Universidade Federal de Goiás; BR; UFG; Mestrado em Engenharia Elétrica e de Computação; Engenharia Publicador: Universidade Federal de Goiás; BR; UFG; Mestrado em Engenharia Elétrica e de Computação; Engenharia
Tipo: Dissertação Formato: application/pdf
POR
Relevância na Pesquisa
66.04%
The Genetic Programming (GP) is a heuristic algorithm for Data Mining (DM), which can be applied to the classification task. This is a method of evolutionary computing inspired in the mechanisms of natural selection theory of Charles Darwin, declared in 1859 in his book "The Origin of Species." From an initial population, the method search over a number of generations to find solutions adapted to the environment of problem. The PG method was proposed in 1990 by John Koza, who demonstrated in one of its applications, the induction in formation of decision trees in the process of data classification. Within this context, the study developed in this work has as main objective the investigation of the concepts of PG and its application on a database of scientific collaboration networks, helping as a management tool in prospective studies of trends for the establishment of common axes in public policy of Science, Technology and Innovation (STI), focusing on regional development. The method is applied on a set of attributes, sorting them in order to identify similarity relationships between groups of researchers that comprise the network. The study involves the concepts of Knowledge Discovery in Databases (KDD) and Data Mining (DM). Networks of Scientific Collaboration...

Método automático para descoberta de funções de ordenação utilizando programação genética paralela em GPU; Automatic raking function discovery method using parallel genetic programming on GPU

Coimbra, Andre Rodrigues
Fonte: Universidade Federal de Goiás; Brasil; UFG; Programa de Pós-graduação em Ciência da Computação (INF); Instituto de Informática - INF (RG) Publicador: Universidade Federal de Goiás; Brasil; UFG; Programa de Pós-graduação em Ciência da Computação (INF); Instituto de Informática - INF (RG)
Tipo: Dissertação Formato: application/pdf
POR
Relevância na Pesquisa
66.08%
Ranking functions have a vital role in the performance of information retrieval systems ensuring that documents more related to the user’s search need – represented as a query – are shown in the top results, preventing the user from having to examine a range of documents that are not really relevant. Therefore, this work uses Genetic Programming (GP), an Evolutionary Computation technique, to find ranking functions automaticaly and systematicaly. Moreover, in this project the technique of GP was developed following a strategy that exploits parallelism through graphics processing units. Other known methods in the context of information retrieval as classification committees and the Lazy strategy were combined with the proposed approach – called Finch. These combinations were only feasible due to the GP nature and the use of parallelism. The experimental results with the Finch, regarding the ranking functions quality, surpassed the results of several strategies known in the literature. Considering the time performance, significant gains were also achieved. The solution developed exploiting the parallelism spends around twenty times less time than the solution using only the central processing unit.; Funções de ordenação têm um papel vital no desempenho de sistemas de recuperação de informação garantindo que os documentos mais relacionados com o desejo do usuário – representado através de uma consulta – sejam trazidos no topo dos resultados...

Automatic Structure Generation using Genetic Programming and Fractal Geometry

Bergen, Steve
Fonte: Brock University Publicador: Brock University
Tipo: Electronic Thesis or Dissertation
ENG
Relevância na Pesquisa
65.98%
Three dimensional model design is a well-known and studied field, with numerous real-world applications. However, the manual construction of these models can often be time-consuming to the average user, despite the advantages o ffered through computational advances. This thesis presents an approach to the design of 3D structures using evolutionary computation and L-systems, which involves the automated production of such designs using a strict set of fitness functions. These functions focus on the geometric properties of the models produced, as well as their quantifiable aesthetic value - a topic which has not been widely investigated with respect to 3D models. New extensions to existing aesthetic measures are discussed and implemented in the presented system in order to produce designs which are visually pleasing. The system itself facilitates the construction of models requiring minimal user initialization and no user-based feedback throughout the evolutionary cycle. The genetic programming evolved models are shown to satisfy multiple criteria, conveying a relationship between their assigned aesthetic value and their perceived aesthetic value. Exploration into the applicability and e ffectiveness of a multi-objective approach to the problem is also presented...

Passive Solar Building Design Using Genetic Programming

Oraei Gholami, Mohammad Mahdi
Fonte: Brock University Publicador: Brock University
Tipo: Electronic Thesis or Dissertation
ENG
Relevância na Pesquisa
66.04%
Passive solar building design is the process of designing a building while considering sunlight exposure for receiving heat in winter and rejecting heat in summer. The main goal of a passive solar building design is to remove or reduce the need of mechanical and electrical systems for cooling and heating, and therefore saving energy costs and reducing environmental impact. This research will use evolutionary computation to design passive solar buildings. Evolutionary design is used in many research projects to build 3D models for structures automatically. In this research, we use a mixture of split grammar and string-rewriting for generating new 3D structures. To evaluate energy costs, the EnergyPlus system is used. This is a comprehensive building energy simulation system, which will be used alongside the genetic programming system. In addition, genetic programming will also consider other design and geometry characteristics of the building as search objectives, for example, window placement, building shape, size, and complexity. In passive solar designs, reducing energy that is needed for cooling and heating are two objectives of interest. Experiments show that smaller buildings with no windows and skylights are the most energy efficient models. Window heat gain is another objective used to encourage models to have windows. In addition...

Inverse Illumination Design with Genetic Programming

Moylan, Kelly
Fonte: Brock University Publicador: Brock University
Tipo: Electronic Thesis or Dissertation
ENG
Relevância na Pesquisa
66.04%
Interior illumination is a complex problem involving numerous interacting factors. This research applies genetic programming towards problems in illumination design. The Radiance system is used for performing accurate illumination simulations. Radiance accounts for a number of important environmental factors, which we exploit during fitness evaluation. Illumination requirements include local illumination intensity from natural and artificial sources, colour, and uniformity. Evolved solutions incorporate design elements such as artificial lights, room materials, windows, and glass properties. A number of case studies are examined, including many-objective problems involving up to 7 illumination requirements, the design of a decorative wall of lights, and the creation of a stained-glass window for a large public space. Our results show the technical and creative possibilities of applying genetic programming to illumination design.

Searching for novel regression functions

Martínez, Yuliana; Galván-López, Edgar
Fonte: IEEE Computer Society Publicador: IEEE Computer Society
Tipo: info:eu-repo/semantics/conferenceObject; all_ul_research; ul_published_reviewed
ENG
Relevância na Pesquisa
45.89%
peer-reviewed; The objective function is the core element in most search algorithms that are used to solve engineering and scientific problems, referred to as the fitness function in evolutionary computation. Some researchers have attempted to bridge this difference by reducing the need for an explicit fitness function. A noteworthy example is the novelty search (NS) algorithm, that substitutes fitness with a measure of uniqueness, or novelty, that each individual introduces into the search. NS employs the concept of behavioral space, where each individual is described by a domain-specific descriptor that captures the main features of an individual’s performance. However, defining a behavioral descriptor is not trivial, and most works with NS have focused on robotics. This paper is an extension of recent attempts to expand the application domain of NS. In particular, it represents the first attempt to apply NS on symbolic regression with genetic programming (GP). The relationship between the proposed NS algorithm and recent semantics-based GP algorithms is explored. Results are encouraging and consistent with recent findings, where NS achieves below average performance on easy problems, and achieves very good performance on hard problems. In summary...

On the design of state-of-the-art pseudorandom number generators by means of genetic programming

Hernández, Julio C.; Seznec, André; Isasi, Pedro
Fonte: IEEE Publicador: IEEE
Tipo: info:eu-repo/semantics/conferenceObject; info:eu-repo/semantics/bookPart Formato: application/pdf
Publicado em /06/2004 ENG
Relevância na Pesquisa
45.99%
The design of pseudorandom number generators by means of evolutionary computation is a classical problem. Today, it has been mostly and better accomplished by means of cellular automata and not many proposals, inside or outside this paradigm could claim to be both robust (passing all the statistical tests, including the most demanding ones) and fast, as is the case of the proposal we present here. Furthermore, for obtaining these generators, we use a radical approach, where our fitness function is not at all based in any measure of randomness, as is frequently the case in the literature, but of nonlinearity. Efficiency is assured by using only very efficient operators (both in hardware and software) and by limiting the number of terminals in the genetic programming implementation.; Congress on Evolutionary Computation. Portland, EEUU, 19-23 June 2004

Evolving hash functions by means of genetic programming

Estébanez, César; Hernández, Julio C.; Ribagorda, Arturo; Isasi, Pedro
Fonte: Association for Computing Machinery Publicador: Association for Computing Machinery
Tipo: info:eu-repo/semantics/conferenceObject; info:eu-repo/semantics/bookPart Formato: application/pdf
Publicado em /07/2006 ENG
Relevância na Pesquisa
66.1%
The design of hash functions by means of evolutionary computation is a relatively new and unexplored problem. In this work, we use Genetic Programming (GP) to evolve robust and fast hash functions. We use a fitness function based on a non-linearity measure, producing evolved hashes with a good degree of Avalanche Effect. Efficiency is assured by using only very fast operators (both in hardware and software) and by limiting the number of nodes. Using this approach, we have created a new hash function, which we call gp-hash, that is able to outperform a set of five human-generated, widely-used hash functions.; This article has been financed by the Spanish founded research MCyT project OP:LINK, Ref:TIN2005-08818-C04-02.; Proceedings of the 8th annual conference on Genetic and evolutionary computation. Seattle, Washington, USA, July 08-12, 2006

Genetic programming for predicting protein networks

García-Jiménez, Beatriz; Aler, Ricardo; Ledezma, Agapito; Sanchis, Araceli
Fonte: Springer Publicador: Springer
Tipo: Conferência ou Objeto de Conferência Formato: application/pdf
Publicado em /10/2008 ENG
Relevância na Pesquisa
65.98%
One of the definitely unsolved main problems in molecular biology is the protein-protein functional association prediction problem. Genetic Programming (GP) is applied to this domain. GP evolves an expression, equivalent to a binary classifier, which predicts if a given pair of proteins interacts. We take advantages of GP flexibility, particularly, the possibility of defining new operations. In this paper, the missing values problem benefits from the definition of if-unknown, a new operation which is more appropriate to the domain data semantics. Besides, in order to improve the solution size and the computational time, we use the Tarpeian method which controls the bloat effect of GP. According to the obtained results, we have verified the feasibility of using GP in this domain, and the enhancement in the search efficiency and interpretability of solutions due to the Tarpeian method.; Proceeding of: 11th Ibero-American Conference on AI (IBERAMIA 2008), Lisbon, Portugal, 14-17 Octubre 2008

Protein-protein functional association prediction using genetic programming

García-Jiménez, Beatriz; Aler, Ricardo; Ledezma, Agapito; Sanchis, Araceli
Fonte: Association for Computing Machinery (ACM) Publicador: Association for Computing Machinery (ACM)
Tipo: Conferência ou Objeto de Conferência Formato: application/pdf
Publicado em //2008 ENG
Relevância na Pesquisa
45.93%
Determining if a group of proteins are functionally associated among themselves is an open problem in molecular biology. Within our long term goal of applying Genetic Programming (GP) to this domain, this paper evaluates the feasibility of GP to predict if a given pair of proteins interacts. GP has been chosen because of its potential flexibility in many aspects, such as the definition of operations. In this paper, the if-unknown operation is defined, which semantically is the most appropriate in this domain for handling missing values. We have also used the Tarpeian bloat control method to decrease the computational time and the solution size. Our results show that GP is feasible for this domain and that the Tarpeian method can obtain large improvements in search efficiency and interpretability of solutions.; Data used in these experiments has been obtained in support of the Structural Computational Biology Group in Spanish National Cancer Research Centre (CNIO). This work has been supported by CICYT (2004-07) TRA2004-07441-C03-02/IA project.; Genetic and Evolutionary Computation Conference, GECCO-08. July 12-16, 2008, Atlanta, Georgia, USA.

A Computational Model Inspired by Gene Regulatory Networks

Lopes, Rui Miguel Lourenço
Fonte: Universidade de Coimbra Publicador: Universidade de Coimbra
Tipo: Tese de Doutorado
ENG
Relevância na Pesquisa
46.02%
Evolutionary Algorithms (EA) are parallel stochastic search procedures that are loosely inspired by the concepts of natural selection and genetic heredity. They have been successfully applied to many domains, and today Evolutionary Computation (EC) attracts a growing number of researchers from the most varied fields. The end of the 20th century brought uncountable discoveries in the biological realm, enabled by the underlying technological breakthroughs. Complete genomes have been sequenced, including the human one, and thanks to the increasing interdisciplinarity of researchers it is known today that there is much more to evolution than just natural selection, namely the influence of the environment, gene regulation, and development. At the core of these processes there is a fundamental piece of complex biological machinery, the Genetic Regulatory Network (GRN). This network results from the interaction amongst the genes and proteins, as well as the environment, governing gene expression and consequently the development of the organism. It is a true fact that the biological knowledge has advanced faster than our ability to incorporate it into the EAs, despite of whether or not it is benificial to do so. One of the main critics pointed-out is that the approach to the genotype-phenotype relationship is different from nature. A lot of effort has been put by some researchers into developing new representations...

An empirical study on the accuracy of computational effort in Genetic Programming

Barrero, David F.; R-Moreno, María Dolores; Castaño, Bonifacio; Camacho, David
Fonte: Institute of Electrical and Electronics Engineers Publicador: Institute of Electrical and Electronics Engineers
Tipo: conferenceObject; bookPart
ENG
Relevância na Pesquisa
45.98%
Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. D. F. Barrero, M. D. R-Moreno, B. Castaño, and D. Camacho, "An empirical study on the accuracy of computational effort in Genetic Programming", in IEEE Congress on Evolutionary Computation (CEC), 2011, pp. 1164 - 1171; Some commonly used performance measures in Genetic Programming are those defined by John Koza in his first book. These measures, mainly computational effort and number of individuals to be processed, estimate the performance of the algorithm as well as the difficulty of a problem. Although Koza's performance measures have been widely used in the literature, their behaviour is not well known. In this paper we study the accuracy of these measures and advance in the understanding of the factors that influence them. In order to achieve this goal, we report an empirical study that attempts to systematically measure the effects of two variability sources in the estimation of the number of individuals to be processed and the computational effort. The results obtained in those experiments suggests that these measures...

Computational Complexity Analysis of Genetic Programming - Initial Results and Future Directions

Neumann, F.; O'Reilly, U.M.; Wagner, M.
Fonte: Springer; United States Publicador: Springer; United States
Tipo: Parte de Livro
Publicado em //2011 EN
Relevância na Pesquisa
66.09%
The computational complexity analysis of evolutionary algorithmsworking on binary strings has significantly increased the rigorous understanding on how these types of algorithm work. Similar results on the computational complexity of genetic programming would fill an important theoretic gap. They would significantly increase the theoretical understanding on how and why genetic programming algorithms work and indicate, in a rigorous manner, how design choices of algorithm components impact its success. We summarize initial computational complexity results for simple tree-based genetic programming and point out directions for future research.; Frank Neumann, Una-May O’Reilly and Markus Wagner; Genetic and Evolutionary Computation Series

Real-Time Automatic Object Classification and Tracking using Genetic Programming and NVIDIA R CUDA TM

Maghoumi, Mehran
Fonte: Brock University Publicador: Brock University
Tipo: Electronic Thesis or Dissertation
ENG
Relevância na Pesquisa
65.98%
Genetic Programming (GP) is a widely used methodology for solving various computational problems. GP's problem solving ability is usually hindered by its long execution times. In this thesis, GP is applied toward real-time computer vision. In particular, object classification and tracking using a parallel GP system is discussed. First, a study of suitable GP languages for object classification is presented. Two main GP approaches for visual pattern classification, namely the block-classifiers and the pixel-classifiers, were studied. Results showed that the pixel-classifiers generally performed better. Using these results, a suitable language was selected for the real-time implementation. Synthetic video data was used in the experiments. The goal of the experiments was to evolve a unique classifier for each texture pattern that existed in the video. The experiments revealed that the system was capable of correctly tracking the textures in the video. The performance of the system was on-par with real-time requirements.

A Field Guide to Genetic Programming

Poli, Ricardo; Langdon, William B.; McPhee, Nicholas F.
Fonte: [S.L.] : Lulu Press (lulu.com), 2008. Publicador: [S.L.] : Lulu Press (lulu.com), 2008.
Tipo: Livro Formato: application/pdf
ENG
Relevância na Pesquisa
66.22%
xiv, 233 p. : il. ; 23 cm.; Libro Electrónico; A Field Guide to Genetic Programming (ISBN 978-1-4092-0073-4) is an introduction to genetic programming (GP). GP is a systematic, domain-independent method for getting computers to solve problems automatically starting from a high-level statement of what needs to be done. Using ideas from natural evolution, GP starts from an ooze of random computer programs, and progressively refines them through processes of mutation and sexual recombination, until solutions emerge. All this without the user having to know or specify the form or structure of solutions in advance. GP has generated a plethora of human-competitive results and applications, including novel scientific discoveries and patentable inventions. The authors; Introduction -- Representation, initialisation and operators in Tree-based GP -- Getting ready to run genetic programming -- Example genetic programming run -- Alternative initialisations and operators in Tree-based GP -- Modular, grammatical and developmental Tree-based GP -- Linear and graph genetic programming -- Probalistic genetic programming -- Multi-objective genetic programming -- Fast and distributed genetic programming -- GP theory and its applications -- Applications -- Troubleshooting GP -- Conclusions.; Contents xi 1 Introduction 1.1 Genetic Programming in a Nutshell 1.2 Getting Started 1.3 Prerequisites 1.4 Overview of this Field Guide I Basics 2 Representation...

Scaling Genetic Programming for Source Code Modification

Cody-Kenny, Brendan; Barrett, Stephen
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 21/11/2012
Relevância na Pesquisa
46.04%
In Search Based Software Engineering, Genetic Programming has been used for bug fixing, performance improvement and parallelisation of programs through the modification of source code. Where an evolutionary computation algorithm, such as Genetic Programming, is to be applied to similar code manipulation tasks, the complexity and size of source code for real-world software poses a scalability problem. To address this, we intend to inspect how the Software Engineering concepts of modularity, granularity and localisation of change can be reformulated as additional mechanisms within a Genetic Programming algorithm.; Comment: 4 pages, Accepted for Graduate Student Workshop, GECCO 2012, Retracted by Authors