Grammatical Evolution (GE) is a method of utilising a general purpose evolutionary algorithm to ???evolve??? programs written in an arbitrary BNF grammar. This thesis extends GE as follows: GE as an extension of Genetic Programming (GP) A novel method of automatically extracting information from the grammar is introduced. This additional information allows the use of GP style crossover which in turn allows GE to perform identically to a strongly typed GP system as well as a non-typed (or canonical) GP system. Two test problems are presented one which is more easily solved by the GP style crossover and one which favours the tradition GE ???Ripple Crossover???. With this new crossover operator GE can now emulate GP (as well as retaining its own unique features) and can therefore now be seen as an extension of GP. Dynamically Defined Functions An extension to the BNF grammar is presented which allows the use of dynamically defined functions (DDFs). DDFs provide an alternative to the traditional approach of Automatically Defined Functions (ADFs) but have the advantage that the number of functions and their parameters do not need to be specified by the user in advance. In addition DDFs allow the architecture of individuals to change dynamically throughout the course of the run without requiring the introduction of any new form of operator. Experimental results are presented confirming the effectiveness of DDFs. Self-Selecting (or variable) crossover. A self-selecting operator is introduced which allows the system to determine, during the course of the run, which crossover operator to apply; this is tested over several problem domains and (especially where small populations are used) is shown to be effective in aiding the system to overcome local optima. Spatial Co-Evolution in Age Layered Planes (SCALP) A method of combining Hornby???s ALPS metaheuristic and a spatial co-evolution system used by Mitchell is presented; the new SCALP system is tested over three problem domains of increasing difficulty and performs extremely well in each of them.

Animals such as bees, ants, birds, fish, and others are able to efficiently perform complex coordinated tasks like foraging, nest-selection, flocking and escaping predators without centralized control or coordination. These complex collective behaviors are the result of emergence. Conventionally, mimicking these collective behaviors with robots requires researchers to study actual behaviors, derive mathematical models, and implement these models as algorithms. Since the conventional approach is very time consuming and cumbersome, this thesis uses an emergence-based method for the efficient evolution of collective behaviors. Our method, Grammatical Evolution algorithm for Evolution of Swarm bEhaviors (GEESE), is based on Grammatical Evolution (GE) and extends the literature on using genetic methods to generate collective behaviors for robot swarms. GEESE uses GE to evolve a primitive set of human-provided rules, represented in a BNF grammar, into productive individual behaviors represented by Behavior Tree (BT). We show that GEESE is generic enough, given an initial grammar, that it can be applied to evolve collective behaviors for multiple problems with just a minor change in objective function. Our method is validated as follows: First, GEESE is compared with state-of-the-art genetic algorithms on the canonical Santa Fe Trail problem. Results show that GEESE outperforms the state-of-the-art by a)~providing better solutions given sufficient population size while b)~utilizing fewer evolutionary steps. Second, GEESE is used to evolve collective swarm behavior for a foraging task. Results show that the evolved foraging behavior using GEESE outperformed both hand-coded solutions as well as solutions generated by conventional Grammatical Evolution. Third, the behaviors evolved for single-source foraging task were able to perform well in a multiple-source foraging task, indicating a type of robustness. Finally, with a minor change to the objective function, the same BNF grammar used for foraging can be shown to evolve solutions to the nest-maintenance and the cooperative transport tasks.

Tree-based genetic programming (GP) has several known shortcomings: difficult adaptability to specific programming languages and environments, the problem of closure and multiple types, and the problem of declarative representation of knowledge. Most of the methods that try to solve these problems are based on formal grammars. The precise effect of their distinctive features is often difficult to analyse and a good comparison of performance in specific problems is missing. This thesis reviews three grammar-based methods: context-free grammar genetic programming (CFG-GP), including its variant GPHH recently applied to exam timetabling, grammatical evolution (GE), and LOGENPRO, it discusses how they solve the problems encountered by GP, and compares them in a series of experiments in six applications using success rates and derivation tree characteristics. The thesis demonstrates that neither GE nor LOGENPRO provide a substantial advantage over CFG-GP in any of the experiments, and analyses the differences between the effects of operators used in CFG-GP and GE. It also presents results from a highly efficient implementation of CFG-GP and GE.

Software testing is an expensive process, which is vital in the industry. Construction of the test-data in software testing requires the major cost and knowing which method to use in order to generate the test data is very important. This paper discusses the performance of search-based algorithms (preferably genetic algorithm) versus random testing, in software test-data generation. A factorial experiment is designed so that, we have more than one factor for each experiment we make. Although many researches have been done in the area of automated software testing, this research differs from all of them due to sample programs (SUTs) which are used. Since the program generation is automatic as well, Grammatical Evolution is used to guide the program generations. They are not goal based, but generated according to the grammar we provide, with different levels of complexity. Genetic algorithm is first applied to programs, then we apply random testing. Based on the results which come up, this paper recommends one method to use for software testing, if the SUT has the same conditions as we had in this study. SUTs are not like the sample programs, provided by other studies since they are generated using a grammar.

The object of my thesis is the realization of grammatical evolution in the Java programming language for solving problems of approximation of functions and synthesis of logical circuits. The application is practical used for testing and gathering data in context of using different purpose function and parallel grammatical evolution. The data are analyzed and evaluated.

Universal class implements grammatical evolution. Tested on approximate functions and settings PSD controller for the chaotic system Henon maps.

Resilience is essential for long-term autonomous agents. Swarm behaviors seen in bees, ants, birds, fish, and others are interesting because they resiliently perform complex coordinated tasks like foraging, nest-selection, flocking and escaping predators without centralized control or coordination. Conventionally, mimicking swarm behaviors with robots requires researchers to study actual behaviors, derive mathematical models, and implement these models as state machines. Since the conventional approach is time-consuming and cumbersome, this dissertation uses a grammatical evolution algorithm with Behavior Trees (BTs) to evolve behaviors that are resilient to different perturbations for foraging and nest maintenance tasks. The modular, reactive, and readable properties of BTs make it an excellent controller for implementing swarm behaviors. Our method is based on the author's master's thesis work on a core algorithm called Grammatical Evolution algorithm for Evolution of Swarm bEhaviors using Behavior Trees (GEESE-BT). The GEESE-BT algorithm can be used to evolve swarm behaviors for interesting multiagent problems, but the solutions require ad hoc fitness functions tailored to the specific problems. This dissertation presents the BeTr-GEESE algorithm, which replaces ad hoc fitness functions with direct feedback from the BT modules. BeTr-GEESE learns more efficiently than GEESE-BT. The modular, subtask-specific programs produced by BeTr-GEESE can be exchanged through lateral transfer to perform missions that require sequential execution of subtasks. Lateral transfer produces resilient performance in divisible and additive group tasks like foraging and nest maintenance. However, the behaviors of successful groups must exhibit temporal locality, meaning that an agent must persist in behavior long enough to perform essential functions but also means that agents cannot persist too long or evolution is too slow. A biologically inspired enhancement of using multiple genes with BeTr-GEESE allowed a fixed population of heterogeneous agents to accomplish tasks with high resilience power and efficiency. The last part of the dissertation complements the empirical approach used in designing resilient swarms using grammatical evolution. Goal specification and verification are vital to designing resilient artificial agents. Finite trace Linear Temporal Logic ($LTL_f$) is a potent way of specifying goals, but synthesizing planners that guarantee the goals are satisfied can be computationally prohibitive. This dissertation shows that goals specified using a subset of finite trace Linear Temporal Logic ($LTL_f$) can be decomposed into an equivalent BT that leads to a relaxed behavior synthesis problem in which a wide range of planners can be used to generate effective behaviors that satisfy the goal specification.

This thesis focuses on the automation of scenarios creation for Portable Stimulus standard. The main goal of the work is an automatic generation of tests, which are defined as graphs for the Questa inFact tool from the Mentor company. For the automation I used an evolutionary algorithm with using a grammatical evolution.  The generated scenarios are connected to the existing verification environment based on UVM methodology, then the verification of the connected component is started. Based on the achieved functional and structural coverage, the individual's fitness value is calculated and propagated into an evolutionary algorithm.  At the end of the work, experiments are performed on the timer component and the contribution of the proposed evolutionary algorithm is evaluated. The proposed evolutionary algorithm is configurable by  grammar and user-defined basic transactions, which allows a wide range of uses. The evolutionary algorithm managed to achieve high functional and structural coverage on the verified timer component.

Στη Διδακτορική Διατριβή μελετώνται 3 κατηγορίες επιθέσεων άρνησης εξυπηρέτησης (Denial-of-Service). Η πρώτη κατηγορία αφορά επιθέσεις τύπου SYN Flood, μια επίθεση που πραγματοποιείται σε χαμηλό επίπεδο και αποτελεί την πιο διαδεδομένη ίσως κατηγορία. Για την αναγνώριση των επιθέσεων αυτών εξήχθησαν 9 στατιστικές παράμετροι οι οποίες τροφοδότησαν τους εξής ταξινομητές: ένα νευρωνικό δίκτυο ακτινικών συναρτήσεων, ένα ταξινομητή κ-κοντινότερων γειτόνων και ένα εξελικτικό νευρωνικό δίκτυο. Ιδιαίτερη σημασία στο σύστημα αναγνώρισης έχουν οι παράμετροι που χρησιμοποιήθηκαν. Για την κατασκευή και επιλογή των παραμέτρων αυτών, προτάθηκε μια νέα τεχνική η οποία χρησιμοποιεί ένα γενετικό αλγόριθμο και μια γραμματική ελεύθερης σύνταξης για να κατασκευάζει νέα σύνολα παραμέτρων από υπάρχοντα σύνολα πρωτογενών χαρακτηριστικών. Στη δεύτερη κατηγορία επιθέσεων, μελετήθηκαν επιθέσεις άρνησης εξυπηρέτησης στην υπηρεσία του παγκόσμιου ιστού (www). Για την αντιμετώπιση των επιθέσεων αυτών προτάθηκε η χρήση υπερσυνδέσμων-παγίδων οι οποίοι τοποθετούνται στον ιστοχώρο και λειτουργούν σαν νάρκες σε ναρκοπέδιο. Οι υπερσύνδεσμοι-παγίδες δεν περιέχουν καμία σημασιολογική πληροφορία και άρα είναι αόρατοι στους πραγματικούς χρήστες ενώ είναι ορατοί στις μηχανές που πραγματοποιούν τις επιθέσεις. Στην τελευταία κατηγορία επιθέσεων, τα μηνύματα ηλεκτρονικού ταχυδρομείου spam, προτάθηκε μια μέθοδος κατασκευής ενός πολύ μικρού αριθμού παραμέτρων και χρησιμοποιήθηκαν για πρώτη φορά νευρωνικά δίκτυα για την αναγνώριση τους. / The dissertation analyzes 3 categories of denial-of-service attacks. The first category concerns SYN Flood attacks, a low level attack which is the most common. For the detection of this type of attacks 9 features were proposed which acted as inputs for the following classifiers: a radial basis function neural network, a k-nearest neighbor classifier and an evolutionary neural network. A crucial part of the proposed system is the parameters that act as inputs for the classifiers. For the selection and construction of those features a new method was proposed that automatically selects constructs new feature sets from a predefined set of primitive characteristics. This new method uses a genetic algorithm and a context-free grammar in order to find the optimal feature set. In the second category, denial-of-service attacks on the World Wide Web service were studied. For the detection of those attacks, the use of decoy-hyperlinks was proposed. Decoy hyperlinks, are hyperlinks that contain no semantic information and thus are invisible to normal users but are transparent to the programs that perform the attacks. The decoys act like mines on a minefield and are placed optimally on the web site so that the detection probability is maximized. In the last type of attack, the email spam problem, a new method was proposed for the construction of a very small number of features which are used to feed a neural network that for the first time is used to detect such attacks.

p, li { white-space: pre-wrap; } Evoluce v přírodě slouží jako zdroj inspirace pro tuto práci . Základní myšlenkou je využití generativní síly gramatik v kombinaci s evolučním přístupem . Nabyté znalosti jsou aplikovány na hledání strategií chování v rozmanitých prostředích . Stromy chování jsou modelem , který bývá běžně použit na řízení rozhodování různých umělých inteligencí . Tato práce se zabývá hledáním stromů chování , které budou řídit jedince řešící nasledující dva problémy : upravenou verzi problému cesty koněm šachovnicí a hraní hry Pirátské kostky . Při hledání strategie hráče kostek , byla použita konkurenční koevoluce . Důvodem je obtížnost návrhu spravedlivé fitness funkce hodnotící výkony hráčů .