Relatedness Measures to Aid the Transfer of Building Blocks among Multiple Tasks

Multitask Learning is a learning paradigm that deals with multiple different tasks in parallel and transfers knowledge among them. XOF, a Learning Classifier System using tree-based programs to encode building blocks (meta-features), constructs and collects features with rich discriminative information for classification tasks in an observed list. This paper seeks to facilitate the automation of feature transferring in between tasks by utilising the observed list. We hypothesise that the best discriminative features of a classification task carry its characteristics. Therefore, the relatedness between any two tasks can be estimated by comparing their most appropriate patterns. We propose a multiple-XOF system, called mXOF, that can dynamically adapt feature transfer among XOFs. This system utilises the observed list to estimate the task relatedness. This method enables the automation of transferring features. In terms of knowledge discovery, the resemblance estimation provides insightful relations among multiple data. We experimented mXOF on various scenarios, e.g. representative Hierarchical Boolean problems, classification of distinct classes in the UCI Zoo dataset, and unrelated tasks, to validate its abilities of automatic knowledge-transfer and estimating task relatedness. Results show that mXOF can estimate the relatedness reasonably between multiple tasks to aid the learning performance with the dynamic feature transferring.Comment: accepted by The Genetic and Evolutionary Computation Conference (GECCO 2020

Distributed classifier migration in XCS for classification of electroencephalographic signals

This paper presents an investigation into combining migration strategies inspired by multi-deme Parallel Genetic Algorithms with the XCS Learning Classifier System to provide parallel and distributed classifier migration. Migrations occur between distributed XCS classifier sub-populations using classifiers ranked according to numerosity, fitness or randomly selected. The influence of the degree-of-connectivity introduced by Fully-Connected, Bi-directional Ring and Uni-directional Ring topologies is examined. Results indicate that classifier migration is an effective method for improving classification accuracy, improving learning speed and reducing final classifier population size, in the single-step classification of noisy, artefact-inclusive human electroencephalographic signals. The experimental results will be used as part of our larger research effort investigating the feasibility of using EEG signals as an interface to allow paralysed persons to control a powered wheelchair or other devices. © 2007 IEEE

XCS Classifier System with Experience Replay

XCS constitutes the most deeply investigated classifier system today. It bears strong potentials and comes with inherent capabilities for mastering a variety of different learning tasks. Besides outstanding successes in various classification and regression tasks, XCS also proved very effective in certain multi-step environments from the domain of reinforcement learning. Especially in the latter domain, recent advances have been mainly driven by algorithms which model their policies based on deep neural networks -- among which the Deep-Q-Network (DQN) is a prominent representative. Experience Replay (ER) constitutes one of the crucial factors for the DQN's successes, since it facilitates stabilized training of the neural network-based Q-function approximators. Surprisingly, XCS barely takes advantage of similar mechanisms that leverage stored raw experiences encountered so far. To bridge this gap, this paper investigates the benefits of extending XCS with ER. On the one hand, we demonstrate that for single-step tasks ER bears massive potential for improvements in terms of sample efficiency. On the shady side, however, we reveal that the use of ER might further aggravate well-studied issues not yet solved for XCS when applied to sequential decision problems demanding for long-action-chains

Classification of EEG signals using a genetic-based machine learning classifier

This paper investigates the efficacy of the genetic-based learning classifier system XCS, for the classification of noisy, artefact-inclusive human electroencephalogram (EEG) signals represented using large condition strings (108bits). EEG signals from three participants were recorded while they performed four mental tasks designed to elicit hemispheric responses. Autoregressive (AR) models and Fast Fourier Transform (FFT) methods were used to form feature vectors with which mental tasks can be discriminated. XCS achieved a maximum classification accuracy of 99.3% and a best average of 88.9%. The relative classification performance of XCS was then compared against four non-evolutionary classifier systems originating from different learning techniques. The experimental results will be used as part of our larger research effort investigating the feasibility of using EEG signals as an interface to allow paralysed persons to control a powered wheelchair or other devices. © 2007 IEEE

INVESTIGATIONS INTO THE COGNITIVE ABILITIES OF ALTERNATE LEARNING CLASSIFIER SYSTEM ARCHITECTURES

The Learning Classifier System (LCS) and its descendant, XCS, are promising paradigms for machine learning design and implementation. Whereas LCS allows classifier payoff predictions to guide system performance, XCS focuses on payoff-prediction accuracy instead, allowing it to evolve optimal classifier sets in particular applications requiring rational thought. This research examines LCS and XCS performance in artificial situations with broad social/commercial parallels, created using the non-Markov Iterated Prisoner\u27s Dilemma (IPD) game-playing scenario, where the setting is sometimes asymmetric and where irrationality sometimes pays. This research systematically perturbs a conventional IPD-playing LCS-based agent until it results in a full-fledged XCS-based agent, contrasting the simulated behavior of each LCS variant in terms of a number of performance measures. The intent is to examine the XCS paradigm to understand how it better copes with a given situation (if it does) than the LCS perturbations studied.Experiment results indicate that the majority of the architectural differences do have a significant effect on the agents\u27 performance with respect to the performance measures used in this research. The results of these competitions indicate that while each architectural difference significantly affected its agent\u27s performance, no single architectural difference could be credited as causing XCS\u27s demonstrated superiority in evolving optimal populations. Instead, the data suggests that XCS\u27s ability to evolve optimal populations in the multiplexer and IPD problem domains result from the combined and synergistic effects of multiple architectural differences.In addition, it is demonstrated that XCS is able to reliably evolve the Optimal Population [O] against the TFT opponent. This result supports Kovacs\u27 Optimality Hypothesis in the IPD environment and is significant because it is the first demonstrated occurrence of this ability in an environment other than the multiplexer and Woods problem domains.It is therefore apparent that while XCS performs better than its LCS-based counterparts, its demonstrated superiority may not be attributed to a single architectural characteristic. Instead, XCS\u27s ability to evolve optimal classifier populations in the multiplexer problem domain and in the IPD problem domain studied in this research results from the combined and synergistic effects of multiple architectural differences

MILCS: A mutual information learning classifier system

This paper introduces a new variety of learning classifier system (LCS), called MILCS, which utilizes mutual information as fitness feedback. Unlike most LCSs, MILCS is specifically designed for supervised learning. MILCS's design draws on an analogy to the structural learning approach of cascade correlation networks. We present preliminary results, and contrast them to results from XCS. We discuss the explanatory power of the resulting rule sets, and introduce a new technique for visualizing explanatory power. Final comments include future directions for this research, including investigations in neural networks and other systems. Copyright 2007 ACM

XCS Algorithms for a Linear Combination of Discounted and Undiscounted Reward Markovian Decision Processes

RÉSUMÉ : Plusieurs études ont montré que combiner certains prédicteurs ensemble peut améliorer la justesse de la prédiction dans certains domaines comme la psychologie, les statistiques ou les sciences du management. Toutefois, aucune de ces études n'ont testé la combinaison de techniques d'apprentissage par renforcement. Notre étude vise à développer un algorithme basé sur deux algorithmes qui sont des formes approximatives d'apprentissage par renforcement répétés dans XCS. Cet algorithme, MIXCS, est une combinaison des techniques de Q-learning et de R-learning pour calculer la combinaison linéaire du payoff résultant des actions de l'agent, et aussi la correspondance entre la prédiction au niveau du système et la valeur réelle des actions de l'agent. MIXCS fait une prévision du payoff espéré pour chacune des actions disponibles pour l'agent. Nous avons testé MIXCS dans deux environnements à deux dimensions, Environment1 et Environment2, qui reproduisent les actions possibles dans un marché financier (acheter, vendre, ne rien faire) pour évaluer les performances d'un agent qui veut obtenir un profit espéré. Nous avons calculé le payoff optimal moyen dans nos deux environnements et avons comparé avec les résultats obtenus par MIXCS. Nous avons obtenu deux résultats. En premier, les résultats de MIXCS sont semblables au payoff optimal moyen pour Environments1, mais pas pour Environment2. Deuxièmement, l'agent obtient le payoff optimal moyen quand il prend l'action "vendre" dans les deux environnements.----------ABSTRACT : Many studies have shown that combining individual predictors improved the accuracy of predictions in different domains such as psychology, statistics and management sciences. However, these studies have not tested the combination of reinforcement learning techniques. This study aims to develop an algorithm based on two iterative approximate forms of reinforcement learning algorithm in XCS. This algorithm, named MIXCS, is a combination of Q-learning and R-learning techniques to compute the linear combination payoff and the correspondence between the system prediction and the action value. As such, MIXCS predicts the payoff to be expected for each possible action. We test MIXCS in two two-dimensional grids called Environment1 and Environment2, which represent financial markets actions of buying, selling and holding to evaluate the performance of an agent as a trader to gain the desired profit. We calculate the optimum average payoff to predict the value of the next movement in both Environment1 and Environment2 and compare the results with those obtained with MIXCS. The results show that the performance of MIXCS is close to optimum average reward in Environment1, but not in Environment2. Also, the agent reaches the maximum reward by taking selling actions in both Environments

Evolutionary computation for software testing

A variety of products undergo a transformation from a pure mechanical design to more and more software and electronic components. A polarized example are watches. Several decades ago they have been purely mechanical. Modern smart watches are almost completely electronic devices which heavily rely on software. Further, a smart watch offers a lot more features than just the information about the current time. This change had a crucial impact on how software is being developed. A first attempt to control the rising complexity was to move to agile development practices such as extreme programming or scrum. This rise in complexity is not only affecting the development process but also quality assurance and software testing. If a product contains more and more features then this leads to a higher number of tests necessary to ensure quality standards. Furthermore agile development practices work in an iterative manner which leads to repetitive testing that puts more effort on the testing team. We aimed within the thesis to ease the pain of testing. Thereby we examined a series of subproblems that arise. A key complexity is the number of test cases. We intended to reduce the number of test cases before they are executed manually or implemented as automated tests. Thereby we examined the test specification and based on the requirements coverage of the individual tests, we were able to identify redundant tests. We relied on a novel metaheuristic called GCAIS which we improved upon iteratively. Another task is to control the remaining complexity. Testing is often time crucial and an appropriate subset of the available tests must be chosen in order to get a quick insight into the status of the device under test. We examined this challenge in two different testing scenarios. The first scenario is located in semi-automated testing where engineers execute a set of automated tests locally and closely observe the behaviour of the system under test. We extended GCAIS to compute test suites that satisfy different criteria if provided with sufficient search time. The second use case is located in fully automated testing in a continuous integration (CI) setting. CI focuses on frequent software build cycles which also include testing. These builds contain a testing stage which greatly emphasizes speed. Thus there we also have to compute crucial tests. However, due to the nature of the process we have to continuously recompute a test suite for each build as the software and maybe even the test cases at hand have changed. Hence it is hard to compute the test suite ahead of time and these tests have to be determined as part of the CI execution. Thus we switched to a computational lightweight learning classifier system (LCS) to prioritize and select test cases. We integrated a series of innovations we made into an LCS known as XCSF such as continuous priorities, experience replay and transfer learning. This enabled us to outperform a state of the art artificial neural network which is used by companies such as Netflix. We further investigated how LCS can be made faster using parallelism. We developed generic approaches which may run on any multicore computing device. This is of interest for our CI use case as the build server's architecture is unknown. However, the methods are also independent of the concrete LCS and are not linked to our testing problem. We identified that many of the challenges that need to be faced in the CI use case have been tackled by Organic Computing (OC), for example the need to adapt to an ever changing environment. Hence we relied on OC design principles to create a system architecture which wraps the LCS developed and integrates it into existing CI processes. The final system is robust and highly autonomous. A side-effect of the high degree of autonomy is a high level of automatization which fits CI well. We also gave insight on the usability and delivery of the full system to our industrial partner. Test engineers can easily integrate it with a few lines of code and need no knowledge about LCS and OC in order to use it. Another implication of the developed system is that OC's ideas and design principles can also be employed outside the field of embedded systems. This shows that OC has a greater level of generality. The process of testing and correcting found errors is still only partially automated. We make a first step into automating the entire process and thereby take an analogy to the concept of self-healing of OC. As a first proof of concept of this school of thought we take a look at touch interfaces. There we can automatically manipulate the software to fulfill the specified behaviour. Thus only a minimalistic amount of manual work is required

Intelligent network intrusion detection using an evolutionary computation approach

With the enormous growth of users\u27 reliance on the Internet, the need for secure and reliable computer networks also increases. Availability of effective automatic tools for carrying out different types of network attacks raises the need for effective intrusion detection systems. Generally, a comprehensive defence mechanism consists of three phases, namely, preparation, detection and reaction. In the preparation phase, network administrators aim to find and fix security vulnerabilities (e.g., insecure protocol and vulnerable computer systems or firewalls), that can be exploited to launch attacks. Although the preparation phase increases the level of security in a network, this will never completely remove the threat of network attacks. A good security mechanism requires an Intrusion Detection System (IDS) in order to monitor security breaches when the prevention schemes in the preparation phase are bypassed. To be able to react to network attacks as fast as possible, an automatic detection system is of paramount importance. The later an attack is detected, the less time network administrators have to update their signatures and reconfigure their detection and remediation systems. An IDS is a tool for monitoring the system with the aim of detecting and alerting intrusive activities in networks. These tools are classified into two major categories of signature-based and anomaly-based. A signature-based IDS stores the signature of known attacks in a database and discovers occurrences of attacks by monitoring and comparing each communication in the network against the database of signatures. On the other hand, mechanisms that deploy anomaly detection have a model of normal behaviour of system and any significant deviation from this model is reported as anomaly. This thesis aims at addressing the major issues in the process of developing signature based IDSs. These are: i) their dependency on experts to create signatures, ii) the complexity of their models, iii) the inflexibility of their models, and iv) their inability to adapt to the changes in the real environment and detect new attacks. To meet the requirements of a good IDS, computational intelligence methods have attracted considerable interest from the research community. This thesis explores a solution to automatically generate compact rulesets for network intrusion detection utilising evolutionary computation techniques. The proposed framework is called ESR-NID (Evolving Statistical Rulesets for Network Intrusion Detection). Using an interval-based structure, this method can be deployed for any continuous-valued input data. Therefore, by choosing appropriate statistical measures (i.e. continuous-valued features) of network trafc as the input to ESRNID, it can effectively detect varied types of attacks since it is not dependent on the signatures of network packets. In ESR-NID, several innovations in the genetic algorithm were developed to keep the ruleset small. A two-stage evaluation component in the evolutionary process takes the cooperation of rules into consideration and results into very compact, easily understood rulesets. The effectiveness of this approach is evaluated against several sources of data for both detection of normal and abnormal behaviour. The results are found to be comparable to those achieved using other machine learning methods from both categories of GA-based and non-GA-based methods. One of the significant advantages of ESR-NIS is that it can be tailored to specific problem domains and the characteristics of the dataset by the use of different fitness and performance functions. This makes the system a more flexible model compared to other learning techniques. Additionally, an IDS must adapt itself to the changing environment with the least amount of configurations. ESR-NID uses an incremental learning approach as new flow of traffic become available. The incremental learning approach benefits from less required storage because it only keeps the generated rules in its database. This is in contrast to the infinitely growing size of repository of raw training data required for traditional learning