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No. of bitstreams: 1 Tese- Patrícia Correia Saraiva.pdf: 5471120 bytes, checksum: faed3fa950294e70e5e4750ea26d9538 (MD5) Previous issue date: 2014-02-28 / FAPEAM - Fundação de Amparo à Pesquisa do Estado do Amazonas / The volume of information encoded in the form of images has increased significantly in the last decades. Contributing to this scenario, the wide-spread use of mobile devices, such as tablets and smartphones, and even notebooks, which not only can take photos, but also easily send them to connected applications, such as web services and social networks. Nowadays, images are used in several applications, such as to record personal moments of people’s life or showing products in e-commerce online stores. As a consequence, not only does the volume of images increase, but also the interest in solutions able to retrieve these images. The main goal of this thesis is to investigate the impact of using genetic programming (GP) as a tool for combining different sources of evidence available when retrieving images. As case studies, we considered the application of GP in two different contexts: image retrieval on the Web using textual information automatically extracted from Web pages, and visual search by expanding the image query using information derived from different types of data, such as text and visual content. We evaluate the proposed expansion strategies in an application of visual search for products focused on e-commerce stores for the fashion domain. Experiments performed in the context of image retrieval on the Web showed that the evolutionary approach outperformed the best baseline with gains of 22.36% in terms of MAP. In the context of visual search for e-commerce applications, experimental results indicated that automatic expansion based on genetic programming is an effective alternative for improving the quality of image search results. When compared to a genetic programming system based only on visual information, the multimodal expansion achieved gains of at least 19% in all scenarios considered. When compared to a similar approach, but completely ad hoc, the GP framework achieved gains of up to 54% in terms of MAP. / O volume de informação codificada sob a forma de imagens tem aumentado de forma significativa nas últimas décadas. O uso cada vez mais frequente de tablets, smartphones, câmeras digitais e notebooks com suporte à aquisição de imagens e a facilidade para tornar essas imagens disponíveis publicamente em repositórios compartilhados, são fatores que contribuem ainda mais para este cenário. Atualmente, imagens são usadas nas mais diversas aplicações, seja para registrar momentos e ações em jornais e revistas eletrônicas, ou redes sociais, ou ainda para divulgar produtos em aplicações de comércio eletrônico. Na medida em que cresce o volume de imagens, cresce também o interesse por sistemas capazes de realizar busca em bases de dados de imagem. O objetivo principal desta tese é investigar o impacto do uso de programação genética (GP - Genetic Progamming) como ferramenta para combinar diferentes fontes de informação disponíveis durante a busca de imagens. Mais especificamente, foram abordados dois contextos distintos como estudos de caso: a busca de imagens na Web utilizando informação textual extraída automaticamente das páginas Web e, a busca visual por meio da expansão da imagem de consulta utilizando informação derivadas de diferentes modalidades de dados, como texto e conteúdo visual. Para avaliar as estratégias propostas para o contexto de busca visual, escolheu-se como estudo de caso a busca visual de produtos em lojas de comércio eletrônico voltadas para o segmento de moda. Os experimentos realizados no contexto de busca de imagens na Web mostraram que a abordagem evolucionária superou a melhor abordagem utilizada como baseline, com ganhos de 22,36% em termos de MAP. No cenário de busca visual de produtos em lojas de comércio eletrônico, os resultados experimentais mostraram que a expansão automática baseada em GP é uma alternativa efetiva para melhorar a qualidade dos resultados de um sistema de busca de imagens. Quando comparado a uma abordagem baseada somente em propriedades visuais, a expansão multimodal obteve ganhos de pelo menos 19% em todos os cenários de busca considerados. Quando comparado a uma abordagem similar, mas completamente ad hoc, o arcabouço baseado em GP obteve ganhos de até 54% em termos de MAP.

In modern computer systems, memory accesses and power management are the two major performance limiting factors. Accesses to main memory are very slow when compared to operations within a processor chip. Hardware write buffers, caches, out-of-order execution, and prefetch logic, are commonly used to reduce the time spent waiting for main memory accesses. Compiler loop interchange and data layout transformations also can help. Unfortunately, large data structures often have access patterns for which none of the standard approaches are useful. Using smaller data structures can significantly improve performance by allowing the data to reside in higher levels of the memory hierarchy. This dissertation proposes using lossy data compression technology called ’Compressive Hashing’ to create “surrogates”, that can augment original large data structures to yield faster typical data access. One way to optimize system performance for power consumption is to provide a predictive control of system-level energy use. This dissertation creates a novel instruction-level cost model called the variable-n-gram model, which is closely related to N-Gram analysis commonly used in computational linguistics. This model does not require direct knowledge of complex architectural details, and is capable of determining performance relationships between instructions from an execution trace. Experimental measurements are used to derive a context-sensitive model for performance of each type of instruction in the context of an N-instruction sequence. Dynamic runtime power prediction mechanisms often suffer from high overhead costs. To reduce the overhead, this dissertation encodes the static instruction-level predictions into a data structure and uses compressive hashing to provide on-demand runtime access to those predictions. Genetic programming is used to evolve compressive hash functions and performance analysis of applications shows that, runtime access overhead can be reduced by a factor of ~3x-9x.

Associative Memories (AMs) are essential building blocks for brain-like intelligent computing with applications in artificial vision, speech recognition, artificial intelligence, and robotics. Computations for such applications typically rely on spatial and temporal associations in the input patterns and need to be robust against noise and incomplete patterns. The conventional method for implementing AMs is through Artificial Neural Networks (ANNs). Improving the density of ANN based on conventional circuit elements poses a challenge as devices reach their physical scalability limits. Furthermore, stored information in AMs is vulnerable to destructive input signals. Novel nano-scale components, such as memristors, represent one solution to the density problem. Memristors are non-linear time-dependent circuit elements with an inherently small form factor. However, novel neuromorphic circuits typically use memristors to replace synapses in conventional ANN circuits. This sub-optimal use is primarily because there is no established design methodology to exploit the memristor's non-linear properties in a more encompassing way. The objective of this thesis is to explore denser and more robust AM designs using memristor networks. We hypothesize that such network AMs will be more area-efficient than the traditional ANN designs if we can use the memristor's non-linear property for spatial and time-dependent temporal association. We have built a comprehensive simulation framework that employs Genetic Programming (GP) to evolve AM circuits with memristors. The framework is based on the ParadisEO metaheuristics API and uses ngspice for the circuit evaluation. Our results show that we can evolve efficient memristor-based networks that have the potential to replace conventional ANNs used for AMs. We obtained AMs that a) can learn spatial and temporal correlation in the input patterns; b) optimize the trade-off between the size and the accuracy of the circuits; and c) are robust against destructive noise in the inputs. This robustness was achieved at the expense of additional components in the network. We have shown that automated circuit discovery is a promising tool for memristor-based circuits. Future work will focus on evolving circuits that can be used as a building block for more complicated intelligent computing architectures.

Chemical reaction networks are an unconventional computing medium that could benefit from the ability to form basic control systems. In this work, we demonstrate the functionality of a chemical control system by evaluating classic genetic algorithm problems: Koza's Santa Fe trail, Jefferson's John Muir trail, and three Santa Fe trail segments. Both Jefferson and Koza found that memory, such as a recurrent neural network or memories in a genetic program, are required to solve the task. Our approach presents the first instance of a chemical system acting as a control system. We propose a delay line connected with an artificial neural network in a chemical reaction network to determine the artificial ant's moves. We first search for the minimal required delay line size connected to a feed forward neural network in a chemical system. Our experiments show a delay line of length four is sufficient. Next, we used these findings to implement a chemical reaction network with a length four delay line and an artificial neural network. We use genetic algorithms to find an optimal set of weights for the artificial neural network. This chemical system is capable of consuming 100% of the food on a subset and greater than 44% of the food on Koza's Santa Fe trail. We also show the first implementation of a simulated chemical memory in two different models that can reliably capture and store information over time. The ability to store data over time gives rise to basic control systems that can perform more complex tasks. The integration of a memory storage unit and a control system in a chemistry has applications in biomedicine, like smart drug delivery. We show that we can successfully store the information over time and use it to act as a memory for a control system navigating an agent through a maze.

<p>In this thesis we address the problem of integrated software pipelining for clustered VLIW architectures. The phases that are integrated and solved as one combined problem are: cluster assignment, instruction selection, scheduling, register allocation and spilling.</p><p>As a first step we describe two methods for integrated code generation of basic blocks. The first method is optimal and based on integer linear programming. The second method is a heuristic based on genetic algorithms.</p><p>We then extend the integer linear programming model to modulo scheduling. To the best of our knowledge this is the first time anybody has optimally solved the modulo scheduling problem for clustered architectures with instruction selection and cluster assignment integrated.</p><p>We also show that optimal spilling is closely related to optimal register allocation when the register files are clustered. In fact, optimal spilling is as simple as adding an additional virtual register file representing the memory and have transfer instructions to and from this register file corresponding to stores and loads.</p><p>Our algorithm for modulo scheduling iteratively considers schedules with increasing number of schedule slots. A problem with such an iterative method is that if the initiation interval is not equal to the lower bound there is no way to determine whether the found solution is optimal or not. We have proven that for a class of architectures that we call transfer free, we can set an upper bound on the schedule length. I.e., we can prove when a found modulo schedule with initiation interval larger than the lower bound is optimal.</p><p>Experiments have been conducted to show the usefulness and limitations of our optimal methods. For the basic block case we compare the optimal method to the heuristic based on genetic algorithms.<em></em></p><p><em>This work has been supported by The Swedish national graduate school in computer science (CUGS) and Vetenskapsrådet (VR).</em></p>

The Interplanetary Magnetic Field (IMF) has a controlling effect on the Magnetosphere and Ionosphere. The objective in this work is to investigate the probable effects of IMF on Ionospheric and Geomagnetic response. To fulfill the objective the concept of an event has been created based on the polarity reversals and rate of change of the interplanetary magnetic field components, Bz and By. Superposed Epoch Method (SPE) was employed with the three event definitions, which are based on IMF Bz southward turnings ranging from 6 to 11 nT in order to quantify the effects of IMF By and Bz. For the first event only IMF Bz turnings were taken into account while for the remaining, positive and negative polarity for IMF By were added. Results showed that the increase in the magnitude of IMF Bz turnings increased the drop of F layer critical frequency, f0F2. The drop was almost linear with the increase in magnitude of polarity reversals. Reversals with a positive IMF By has resulted in the continuation of geomagnetic activity more than 4 days, that is to say, the energy, that has penetrated as a consequence of reversal with a positive By polarity, was stored in outer Magnetosphere,whereas, with a negative IMF By the energy was consumed in a small time scale. At the second step of the work, although conclusions about geomagnetic activity could be done, as a consequence of data gaps for f0F2 in addition to having low numbers of events, characterization of f0F2 due to constant IMF By polarity could not be accomplished. Thus, a modeling attempt for the characterization of the response due to polarity reversals of IMF components with the Genetic Programming was carried out. Four models were constructed for different polarity reversal cases and they were used as the components of one general unique model. The model is designed in such a way that given 3 consecutive value of f0F2, IMF By and IMF Bz, the model can forecast one hour ahead value of f0F2. The overall model, GETY-IYON was successful at a normalized error of 7.3%.

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 program. Thus the goal of program discovery as a solution for a problem is addressed by GP in the framework of evolutionary computation. In this thesis, we address for the first time the problem of applying GP to mu1ticategory pattern classification. In supervised pattern classification, an input vector of m dimensions is mapped onto one of the n classes. It has a number of application areas such as remote sensing, medical diagnosis etc., A supervised classifier is developed by using a training set that contains representative samples of various classes present in the application. Supervised classification has been done earlier with maximum likelihood classifier: neural networks and fuzzy logic. The major considerations in applying GP to pattern classification are listed below: (i) GP-based techniques are data distribution-free i.e., no a priori knowledge is needed abut the statistical distribution of the data or no assumption such as normal distribution for data needs to be made as in MLC. (ii) GP can directly operate on the data in its original form. (iii) GP can detect the underlying but unknown relationship that mists among data and express it as a mathematical LISP S-expression. The generated LISP S-expressions can be directly used in the application environment. (iv) GP can either discover the most important discriminating features of a class during evolution or it requires minor post-processing of the LISP-S expression to discover the discriminant features. In a neural network, the knowledge learned by the neural network about the data distributions is embedded in the interconnection weights and it requires considerable amount of post-processing of the weights to understand the decision of the neural network. In 2-category pattern classification, a single GP expression is evolved as a discriminant function. The output of the GP expression can be +l for samples of one class and -1 for samples of the other class. When the GP paradigm is applied to an n-class problem, the following questions arise: Ql. As a typical GP expression returns a value (+l or -1) for a 2-class problem, how does one apply GP for the n-class pattern classification problem? Q2. What should be the fitness function during evolution of the GP expressions? Q3. How does the choice of a function set affect the performance of GP-based classification? Q4. How should training sets be created for evaluating fitness during the evolution of GP classifier expressions? Q5. How does one improve learning of the underlying data distributions in a GP framework? Q6. How should conflict resolution be handled before assigning a class to the input feature vector? Q7. How does GP compare with other classifiers for an n-class pattern classification problem? The research described here seeks to answer these questions. We show that GP can be applied to an n-category pattern classification problem by considering it as n 2-class problems. The suitability of this approach is demonstrated by considering a real-world problem based on remotely sensed satellite images and Fisher's Iris data set. In a 2-class problem, simple thresholding is sufficient for a discriminant function to divide the feature space into two regions. This means that one genetic programming classifier expression (GPCE) is sufficient to say whether or not the given input feature vector belongs to that class; i.e., the GP expression returns a value (+1 or -1). As the n-class problem is formulated as n 2-class problems, n GPCEs are evolved. Hence, n GPCE specific training sets are needed to evolve these n GPCEs. For the sake of illustration, consider a 5-class pat tern classification problem. Let n, be the number of samples that belong to class j, and N, be the number of samples that do not belong to class j, (j = 1,..., 5). Thus, N1=n2+n3+n4+n5 N2=n1+n3+n4+n5 N3=n1+n2+n4+n5 N4=n1+n2+n3+n5 N5=n1+n2+n3+n4 Thus, When the five class problem is formulated as five 2-class problems. we need five GPCEs as discriminant functions to resolve between n1 and N1, n2 and N2, n3 and N3, n4 and N4 and lastly n5 and N5. Each of these five 2-class problems is handled as a separate 2-class problem with simple thresholding. Thus, GPCE# l resolves between samples of class# l and the remaining n - 1 classes. A training set is needed to evaluate the fitness of GPCE during its evolution. If we directly create the training set, it leads to skewness (as n1 < N1). To overcome the skewness, an interleaved data format is proposed for the training set of a GPCE. For example, in the training set of GPCE# l, samples of class# l are placed alternately between samples of the remaining n - 1 classes. Thus, the interleaved data format is an artifact to create a balanced training set. Conventionally, all the samples of a training set are fed to evaluate the fitness of every member of the population in each generation. We call this "global" learning 3s GP tries to learn the entire training set at every stage of the evolution. We have introduced incremental learning to simplify the task of learning for the GP paradigm. A subset of the training set is fed and the size of the subset is gradually increased over time to cover the entire training data. The basic motivation for incremental learning is to improve learning during evolution as it is easier to learn a smaller task and then to progress from a smaller task to a bigger task. Experimental results are presented to show that the interleaved data format and incremental learning improve the performance of the GP classifier. We also show that the GPCEs evolved with an arithmetic function set are able to track variation in the input better than GPCEs evolved with function sets containing logical and nonlinear elements. Hence, we have used arithmetic function set, incremental learning, and interleaved data format to evolve GPCEs in our simulations. AS each GPCE is trained to recognize samples belonging to its own class and reject samples belonging to other classes a strength of association measure is associated with each GPCE to indicate the degree to which it can recognize samples belonging to its own class. The strength of association measures are used for assigning a class to an input feature vector. To reduce misclassification of samples, we also show how heuristic rules can be generated in the GP framework unlike in either MLC or the neural network classifier. We have also studied the scalability and generalizing ability of the GP classifier by varying the number of classes. We also analyse the performance of the GP classifier by considering the well-known Iris data set. We compare the performance of classification rules generated from the GP classifier with those generated from neural network classifier, (24.5 method and fuzzy classifier for the Iris data set. We show that the performance of GP is comparable to other classifiers for the Iris data set. We notice that the classification rules can be generated with very little post-processing and they are very similar to the rules generated from the neural network and C4.5 for the Iris data set. Incremental learning influences the number of generations available for GP to learn the data distribution of classes whose d is -1 in the interleaved data format. This is because the samples belonging to the true class (desired output d is +1) are alternately placed between samples belonging to other classes i.e., they are repeated to balance the training set in the interleaved data format. For example, in the evolution of GPCE for class# l, the fitness function can be fed initially with samples of class#:! and subsequently with the samples of class#3, class#4 and class#. So in the evaluation of the fitness function, the samples of class#kt5 will not be present when the samples of class#2 are present in the initial stages. However, in the later stages of evolution, when samples of class#5 are fed, the fitness function will utilize the samples of both class#2 and class#5. As learning in evolutionary computation is guided by the evaluation of the fitness function, GPCE# l gets lesser number of generations to learn how to reject data of class#5 as compared to the data of class#2. This is because the termination criterion (i.e., the maximum number of generations) is defined a priori. It is clear that there are (n-l)! Ways of ordering the samples of classes whose d is -1 in the interleaved data format. Hence a heuristic is presented to determine a possible order to feed data of different classes for the GPCEs evolved with incremental learning and interleaved data format. The heuristic computes an overlap index for each class based on its spatial spread and distribution of data in the region of overlap with respect to other classes in each feature. The heuristic determines the order in which classes whose desired output d is –1 should be placed in each GPCE-specific training set for the interleaved data format. This ensures that GP gets more number of generations to learn about the data distribution of a class with higher overlap index than a class with lower overlap index. The ability of the GP classifier to learn the data distributions depends upon the number of classes and the spatial spread of data. As the number of classes increases, the GP classifier finds it difficult to resolve between classes. So there is a need to partition the feature space and identify subspaces with reduced number of classes. The basic objective is to divide the feature space into subspaces and hence the data set that contains representative samples of n classes into subdata sets corresponding to the subspaces of the feature space, so that some of the subdata sets/spaces can have data belonging to only p classes (p < n). The GP classifier is then evolved independently for the subdata sets/spaces of the feature space. This results in localized learning as the GP classifier has to learn the data distribution in only a subspace of the feature space rather than in the entire feature space. By integrating the GP classifier with feature space partitioning (FSP), we improve classification accuracy due to localized learning. Although serial computers have increased steadily in their performance, the quest for parallel implementation of a given task has continued to be of interest in any computationally intensive task since parallel implementation leads to a faster execution than a serial implementation As fitness evaluation, selection strategy and population structures are used to evolve a solution in GP, there is scope for a parallel implementation of GP classifier. We have studied distributed GP and massively parallel GP for our approach to GP-based multicategory pattern classification. We present experimental results for distributed GP with Message Passing Interface on IBM SP2 to highlight the speedup that can be achieved over the serial implementation of GP. We also show how data parallelism can be used to further speed up fitness evaluation and hence the execution of the GP paradigm for multicategory pat tern classification. We conclude that GP can be applied to n-category pattern classification and its potential lies in its simplicity and scope for parallel implementation. The GP classifier developed in this thesis can be looked upon as an addition to the earlier statistical, neural and fuzzy approaches to multicategory pattern classification.

Thesis (Ph. D.)--Brandeis University, 2009. / "UMI:3369218." MICROFILM COPY ALSO AVAILABLE IN THE UNIVERSITY ARCHIVES. Includes bibliographical references.

Aquatic ecosystems are an essential source for life and, in many regions, are exploited to a degree which deteriorates their ecological status. Today, more than 50 % of the European lakes suffer from an ecological status which is unsatisfactory. Many of these lakes require abatement actions to improve their status, and mathematical models have a great potential to predict and evaluate different abatement actions and their outcome. Several statistical methods and models exist which can be used for these purposes; however, many of the models are not constructed using a sufficient amount or quality of data, are too complex to be used by most managers, or are too site specific. Therefore, the main aim of this thesis was to present different statistical methods and models which are easy to use by managers, are general, and provide insights for the development of similar methods and models. To reach the main aim of the thesis several different statistical and modelling procedures were investigated and applied, such as genetic programming (GP), multiple regression, Markov Chains, and finally, well-used criteria for the r2 and p-value for the development of a method to determine temporal-trends. The statistical methods and models were mainly based on the variables chlorophyll-a (chl-a) and total phosphorus (TP) concentrations, but some methods and models can be directly transferred to other variables. The main findings in this thesis were that multiple regressions overcome the performance of GP to predict summer chl-a concentrations and that multiple regressions can be used to generally describe the chl-a seasonality with TP summer concentrations and the latitude as independent variables. Also, it is possible to calculate probabilities, using Markov Chains, of exceeding certain chl-a concentrations in future months. Results showed that deep water concentrations were in general closely related to the surface water concentrations along with morphometric parameters; these independent variables can therefore be used in mass-balance models to estimate the mass in deep waters. A new statistical method was derived and applied to confirm whether variables have changed over time or not for cases where other traditional methods have failed. Finally, it is concluded that the statistical methods and models developed in this thesis will increase the understanding for predictions within and across aquatic systems.

Η αυτοματοποιημένη μεθοδολογία εύρεσης προγραμμάτων υπολογιστών (κώδικα) που βασίζεται στις αρχές της βιολογικής εξέλιξης, ονομάζεται Γενετικός Προγραμματισμός (ΓΠ). Με άλλα λόγια, πρόκειται για μια τεχνική Μηχανικής Μάθησης, η οποία χρησιμοποιεί ένα Εξελικτικό Αλγόριθμο για να βελτιστοποιήσει ένα πληθυσμό από προγράμματα υπολογιστή σύμφωνα με μια συνάρτηση καταλληλότητας που καθορίζεται από την ικανότητα του προγράμματος να εκτελέσει ένα δοσμένο υπολογιστικό έργο. Στην εργασία αυτή θα χρησιμοποιηθούν διάφορες τεχνικές Γενετικού Προγραμματισμού στην μοντελοποίηση Χρονοσειρών. Τα συστήματα που θα αναπτυχθούν, θα χρησιμοποιηθούν για τους παρακάτω σκοπούς: • Μοντελοποίηση του συστήματος που «παράγει» τη χρονοσειρά, • Εξαγωγή χαρακτηριστικών και κανόνων που μπορούν να οδηγήσουν στην ικανότητα πρόβλεψης χρονοσειρών. Οι χρονοσειρές που θα χρησιμοποιηθούν για να δοκιμάσουμε την λειτουργία των συστημάτων που θα υλοποιηθούν είναι οι εξής: • Χρονοσειρά δεικτών ελληνικού χρηματιστηρίου, • Χρονοσειρές ιατρικών δεδομένων όπως για παράδειγμα χρονοσειρά σήματος μαγνητοεγκεφαλογραφήματος. Οι κλασσικές τεχνικές Γενετικού Προγραμματισμού χρησιμοποιούν δενδρικές δομές για την αναπαράσταση των προγραμμάτων-ατόμων των πληθυσμών. Στο παρελθόν έχουν εκπονηθεί και υλοποιηθεί πολλές εργασίες που χρησιμοποιούν γενετικό προγραμματισμό για την μοντελοποίηση χρονοσειρών. Τα αποτελέσματα ήταν ικανοποιητικά. Το βασικό πρόβλημα που αντιμετωπίστηκε ήταν ο μεγάλος χρόνος εκτέλεσης που απαιτούν οι κλασσικές τεχνικές Γενετικού προγραμματισμού. Το θέμα λοιπόν είναι ανοιχτό σε μελέτη και υπάρχει η ανάγκη να χρησιμοποιηθούν νέες τεχνικές γενετικού προγραμματισμού για να πάρουμε και καλύτερα και πιο γρήγορα αποτελέσματα. Στην εργασία αυτή, θα χρησιμοποιηθεί η τεχνική του Γραμμικού Γενετικού Προγραμματισμού. Σε αυτήν την τεχνική, τα προγράμματα-άτομα του πληθυσμού αναπαρίστανται σαν μια ακολουθία από εντολές οι οποίες αναπαρίστανται σε δυαδική μορφή. Οι δύο αυτές τεχνικές θα συγκριθούν και θα βγουν συμπεράσματα για το ποια είναι η πιο χρήσιμη στον τομέα της μοντελοποίησης χρονοσειρών. Ακόμη, θα υλοποιηθούν αλγόριθμοι οι οποίοι εντοπίζουν και αφαιρούν τον κώδικα που δεν συμμετέχει στην παραγωγή της εξόδου των προγραμμάτων-ατόμων του πληθυσμού. Οι αλγόριθμοι αυτοί, περιμένουμε να επιταχύνουν κατά πολύ την διαδικασία της εξέλιξης του πληθυσμού, αφού στον γενετικό προγραμματισμό σχηματίζονται συχνά τέτοια μπλοκ κώδικα που δεν επηρεάζουν την έξοδο των προγραμμάτων. / -