Analysis of Linkage-Friendly Genetic Algorithms

Evolutionary algorithms (EAs) are stochastic population-based algorithms inspired by the natural processes of selection, mutation, and recombination. EAs are often employed as optimum seeking techniques. A formal framework for EAs is proposed, in which evolutionary operators are viewed as mappings from parameter spaces to spaces of random functions. Formal definitions within this framework capture the distinguishing characteristics of the classes of recombination, mutation, and selection operators. EAs which use strictly invariant selection operators and order invariant representation schemes comprise the class of linkage-friendly genetic algorithms (lfGAs). Fast messy genetic algorithms (fmGAs) are lfGAs which use binary tournament selection (BTS) with thresholding, periodic filtering of a fixed number of randomly selected genes from each individual, and generalized single-point crossover. Probabilistic variants of thresholding and filtering are proposed. EAs using the probabilistic operators are generalized fmGAs (gfmGAs). A dynamical systems model of lfGAs is developed which permits prediction of expected effectiveness. BTS with probabilistic thresholding is modeled at various levels of abstraction as a Markov chain. Transitions at the most detailed level involve decisions between classes of individuals. The probability of correct decision making is related to appropriate maximal order statistics, the distributions of which are obtained. Existing filtering models are extended to include probabilistic individual lengths. Sensitivity of lfGA effectiveness to exogenous parameters limits practical applications. The lfGA parameter selection problem is formally posed as a constrained optimization problem in which the cost functional is related to expected effectiveness. Kuhn-Tucker conditions for the optimality of gfmGA parameters are derived

Phylogenetics

Describing the diversity of living beings has always instigated man. The classification proposed by Aristotle today seems naïve and unnatural, but it lasted from ancient Greece until the publication of the Linnaeus Systema Naturae in 1758. Although quite accurate, the taxonomic classification proposed by naturalist Carl Linnaeus did not consider the evolutionary relationships between living beings. This view, although prior to Charles Darwin, only gained deserved prominence after On the Origin of Species. Only in the twentieth century, a new area founded by Hennig, phylogenetic systematics was implemented, and with this, a series of useful methods in the construction of phylogenetic trees arose, as maximum parsimony, neighbor joining, UPGMA, maximum likelihood, and Bayesian inference. With the advancement of information technology, phylogenetic analyses have become more sophisticated and faster. The algorithms used in the analysis programs have become more complex and realistic, favoring the addition of substitution models. The application of these data and the greater facility in generating nucleotide and amino acid sequences allowed the comparison previously unimaginable, for example, between bacteria and eukaryotes. In this way, the history of the advances of phylogenetic knowledge is confused with the greater knowledge about the origin of life

Paraiso : An Automated Tuning Framework for Explicit Solvers of Partial Differential Equations

We propose Paraiso, a domain specific language embedded in functional programming language Haskell, for automated tuning of explicit solvers of partial differential equations (PDEs) on GPUs as well as multicore CPUs. In Paraiso, one can describe PDE solving algorithms succinctly using tensor equations notation. Hydrodynamic properties, interpolation methods and other building blocks are described in abstract, modular, re-usable and combinable forms, which lets us generate versatile solvers from little set of Paraiso source codes. We demonstrate Paraiso by implementing a compressive hydrodynamics solver. A single source code less than 500 lines can be used to generate solvers of arbitrary dimensions, for both multicore CPUs and GPUs. We demonstrate both manual annotation based tuning and evolutionary computing based automated tuning of the program.Comment: 52 pages, 14 figures, accepted for publications in Computational Science and Discover

AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments

This report considers the application of Articial Intelligence (AI) techniques to the problem of misuse detection and misuse localisation within telecommunications environments. A broad survey of techniques is provided, that covers inter alia rule based systems, model-based systems, case based reasoning, pattern matching, clustering and feature extraction, articial neural networks, genetic algorithms, arti cial immune systems, agent based systems, data mining and a variety of hybrid approaches. The report then considers the central issue of event correlation, that is at the heart of many misuse detection and localisation systems. The notion of being able to infer misuse by the correlation of individual temporally distributed events within a multiple data stream environment is explored, and a range of techniques, covering model based approaches, `programmed' AI and machine learning paradigms. It is found that, in general, correlation is best achieved via rule based approaches, but that these suffer from a number of drawbacks, such as the difculty of developing and maintaining an appropriate knowledge base, and the lack of ability to generalise from known misuses to new unseen misuses. Two distinct approaches are evident. One attempts to encode knowledge of known misuses, typically within rules, and use this to screen events. This approach cannot generally detect misuses for which it has not been programmed, i.e. it is prone to issuing false negatives. The other attempts to `learn' the features of event patterns that constitute normal behaviour, and, by observing patterns that do not match expected behaviour, detect when a misuse has occurred. This approach is prone to issuing false positives, i.e. inferring misuse from innocent patterns of behaviour that the system was not trained to recognise. Contemporary approaches are seen to favour hybridisation, often combining detection or localisation mechanisms for both abnormal and normal behaviour, the former to capture known cases of misuse, the latter to capture unknown cases. In some systems, these mechanisms even work together to update each other to increase detection rates and lower false positive rates. It is concluded that hybridisation offers the most promising future direction, but that a rule or state based component is likely to remain, being the most natural approach to the correlation of complex events. The challenge, then, is to mitigate the weaknesses of canonical programmed systems such that learning, generalisation and adaptation are more readily facilitated

Combined optimization algorithms applied to pattern classification

Accurate classification by minimizing the error on test samples is the main goal in pattern classification. Combinatorial optimization is a well-known method for solving minimization problems, however, only a few examples of classifiers axe described in the literature where combinatorial optimization is used in pattern classification. Recently, there has been a growing interest in combining classifiers and improving the consensus of results for a greater accuracy. In the light of the "No Ree Lunch Theorems", we analyse the combination of simulated annealing, a powerful combinatorial optimization method that produces high quality results, with the classical perceptron algorithm. This combination is called LSA machine. Our analysis aims at finding paradigms for problem-dependent parameter settings that ensure high classifica, tion results. Our computational experiments on a large number of benchmark problems lead to results that either outperform or axe at least competitive to results published in the literature. Apart from paxameter settings, our analysis focuses on a difficult problem in computation theory, namely the network complexity problem. The depth vs size problem of neural networks is one of the hardest problems in theoretical computing, with very little progress over the past decades. In order to investigate this problem, we introduce a new recursive learning method for training hidden layers in constant depth circuits. Our findings make contributions to a) the field of Machine Learning, as the proposed method is applicable in training feedforward neural networks, and to b) the field of circuit complexity by proposing an upper bound for the number of hidden units sufficient to achieve a high classification rate. One of the major findings of our research is that the size of the network can be bounded by the input size of the problem and an approximate upper bound of 8 + √2n/n threshold gates as being sufficient for a small error rate, where n := log/SL and SL is the training set

Boosting Perturbation-Based Iterative Algorithms to Compute the Median String

[Abstract] The most competitive heuristics for calculating the median string are those that use perturbation-based iterative algorithms. Given the complexity of this problem, which under many formulations is NP-hard, the computational cost involved in the exact solution is not affordable. In this work, the heuristic algorithms that solve this problem are addressed, emphasizing its initialization and the policy to order possible editing operations. Both factors have a significant weight in the solution of this problem. Initial string selection influences the algorithm’s speed of convergence, as does the criterion chosen to select the modification to be made in each iteration of the algorithm. To obtain the initial string, we use the median of a subset of the original dataset; to obtain this subset, we employ the Half Space Proximal (HSP) test to the median of the dataset. This test provides sufficient diversity within the members of the subset while at the same time fulfilling the centrality criterion. Similarly, we provide an analysis of the stop condition of the algorithm, improving its performance without substantially damaging the quality of the solution. To analyze the results of our experiments, we computed the execution time of each proposed modification of the algorithms, the number of computed editing distances, and the quality of the solution obtained. With these experiments, we empirically validated our proposal.This work was supported in part by the Comisión Nacional de Investigación Científica y Tecnológica - Programa de Formación de Capital Humano Avanzado (CONICYT-PCHA)/Doctorado Nacional/2014-63140074 through the Ph.D. Scholarship, in part by the European Union's Horizon 2020 under the Marie Sklodowska-Curie under Grant 690941, in part by the Millennium Institute for Foundational Research on Data (IMFD), and in part by the FONDECYT-CONICYT under Grant 1170497. The work of ÓSCAR PEDREIRA was supported in part by the Xunta de Galicia/FEDER-UE refs under Grant CSI ED431G/01 and Grant GRC: ED431C 2017/58, in part by the Office of the Vice President for Research and Postgraduate Studies of the Universidad Católica de Temuco, VIPUCT Project 2020EM-PS-08, and in part by the FEQUIP 2019-INRN-03 of the Universidad Católica de TemucoXunta de Galicia; ED431G/01Xunta de Galicia; ED431C 2017/58Chile. Comisión Nacional de Investigación Científica y Tecnológica; 2014-63140074Chile. Comisión Nacional de Investigación Científica y Tecnológica; 1170497Universidad Católica de Temuco (Chile); 2020EM-PS-08Universidad Católica de Temuco (Chile); 2019-INRN-0

Compilers that learn to optimise: a probabilistic machine learning approach

Compiler optimisation is the process of making a compiler produce better code, i.e. code that, for example, runs faster on a target architecture. Although numerous program transformations for optimisation have been proposed in the literature, these transformations are not always beneficial and they can interact in very complex ways. Traditional approaches adopted by compiler writers fix the order of the transformations and decide when and how these transformations should be applied to a program by using hard-coded heuristics. However, these heuristics require a lot of time and effort to construct and may sacrifice performance on programs they have not been tuned for.This thesis proposes a probabilistic machine learning solution to the compiler optimisation problem that automatically determines "good" optimisation strategies for programs. This approach uses predictive modelling in order to search the space of compiler transformations. Unlike most previous work that learns when/how to apply a single transformation in isolation or a fixed-order set of transformations, the techniques proposed in this thesis are capable of tackling the general problem of predicting "good" sequences of compiler transformations. This is achieved by exploiting transference across programs with two different techniques: Predictive Search Distributions (PSD) and multi-task Gaussian process prediction (multi-task GP). While the former directly addresses the problem of predicting "good" transformation sequences, the latter learns regression models (or proxies) of the performance of the programs in order to rapidly scan the space of transformation sequences.Both methods, PSD and multi-task GP, are formulated as general machine learning techniques. In particular, the PSD method is proposed in order to speed up search in combinatorial optimisation problems by learning a distribution over good solutions on a set of problem in¬ stances and using that distribution to search the optimisation space of a problem that has not been seen before. Likewise, multi-task GP is proposed as a general method for multi-task learning that directly models the correlation between several machine learning tasks, exploiting the shared information across the tasks.Additionally, this thesis presents an extension to the well-known analysis of variance (ANOVA) methodology in order to deal with sequence data. This extension is used to address the problem of optimisation space characterisation by identifying and quantifying the main effects of program transformations and their interactions.Finally, the machine learning methods proposed are successfully applied to a data set that has been generated as a result of the application of source-to-source transformations to 12 C programs from the UTDSP benchmark suite

Unsupervised Intrusion Detection with Cross-Domain Artificial Intelligence Methods

Cybercrime is a major concern for corporations, business owners, governments and citizens, and it continues to grow in spite of increasing investments in security and fraud prevention. The main challenges in this research field are: being able to detect unknown attacks, and reducing the false positive ratio. The aim of this research work was to target both problems by leveraging four artificial intelligence techniques. The first technique is a novel unsupervised learning method based on skip-gram modeling. It was designed, developed and tested against a public dataset with popular intrusion patterns. A high accuracy and a low false positive rate were achieved without prior knowledge of attack patterns. The second technique is a novel unsupervised learning method based on topic modeling. It was applied to three related domains (network attacks, payments fraud, IoT malware traffic). A high accuracy was achieved in the three scenarios, even though the malicious activity significantly differs from one domain to the other. The third technique is a novel unsupervised learning method based on deep autoencoders, with feature selection performed by a supervised method, random forest. Obtained results showed that this technique can outperform other similar techniques. The fourth technique is based on an MLP neural network, and is applied to alert reduction in fraud prevention. This method automates manual reviews previously done by human experts, without significantly impacting accuracy