Quantum Genetic Algorithms for Computer Scientists

Genetic algorithms (GAs) are a class of evolutionary algorithms inspired by Darwinian natural selection. They are popular heuristic optimisation methods based on simulated genetic mechanisms, i.e., mutation, crossover, etc. and population dynamical processes such as reproduction, selection, etc. Over the last decade, the possibility to emulate a quantum computer (a computer using quantum-mechanical phenomena to perform operations on data) has led to a new class of GAs known as “Quantum Genetic Algorithms” (QGAs). In this review, we present a discussion, future potential, pros and cons of this new class of GAs. The review will be oriented towards computer scientists interested in QGAs “avoiding” the possible difficulties of quantum-mechanical phenomena

Exploiting Tournament Selection-Based Genetic Algorithm in Integrated AHP-Taguchi Analyses-GA Method for Wire Electrical Discharge Machining of AZ91 Magnesium Alloy

Concurrent optimization and prioritization of wire EDM parameters can improve resource allocations in material processing and should be effective. This study advances the integrated analytic (AHP)-Taguchi(T)-tournament-based-genetic algorithm (tGA) method to moderate the influence of erroneous resource allocation in parametric analysis decisions in wire electrical discharge machining. The structure builds on the AHP-T method’s platform obtained from the literature and develops it by including the tGA while processing the AZ91 magnesium alloy. The article evaluates the delta values for the average signal-to-noise ratios in the response table and deploys them to arrive at the winners in a league and consequently mutate the chromosomes for performance improvement. The scale of relative importance, consistency index, optimal parametric setting, delta values, and ranks are all established and coupled with the total value and maximum value evaluation at the selection crossover and mutation stages of the genetic algorithm. The results at the mutation, crossover, and selection stages of the tournament selection process showed total values of 124410, 96650, and 70564, respectively. At the selection stage, the maximum value to be the winner of the tournament is 28704. The crossover operation was accomplished after the 5th, 5th, and 6th bit for the first three pairs, respectively. For the selection and crossover operations, the maximum value is 28604 and 27944, respectively. The research clarifies which parameters are the best and worst during optimization using the AHP-T-tGA method

Fraud detection in the banking sector : a multi-agent approach

Fraud is an increasing phenomenon as shown in many surveys carried out by leading international consulting companies in the last years. Despite the evolution of electronic payments and hacking techniques there is still a strong human component in fraud schemes. Conflict of interest in particular is the main contributing factor to the success of internal fraud. In such cases anomaly detection tools are not always the best instruments, since the fraud schemes are based on faking documents in a context dominated by lack of controls, and the perpetrators are those ones who should control possible irregularities. In the banking sector audit team experts can count only on their experience, whistle blowing and the reports sent by their inspectors. The Fraud Interactive Decision Expert System (FIDES), which is the core of this research, is a multi-agent system built to support auditors in evaluating suspicious behaviours and to speed up the evaluation process in order to detect or prevent fraud schemes. The system combines Think-map, Delphi method and Attack trees and it has been built around audit team experts and their needs. The output of FIDES is an attack tree, a tree-based diagram to ”systematically categorize the different ways in which a system can be attacked”. Once the attack tree is built, auditors can choose the path they perceive as more suitable and decide whether or not to start the investigation. The system is meant for use in the future to retrieve old cases in order to match them with new ones and find similarities. The retrieving features of the system will be useful to simplify the risk management phase, since similar countermeasures adopted for past cases might be useful for present ones. Even though FIDES has been built with the banking sector in mind, it can be applied in all those organisations, like insurance companies or public organizations, where anti-fraud activity is based on a central anti-fraud unit and a reporting system

Machine learning for network based intrusion detection: an investigation into discrepancies in findings with the KDD cup '99 data set and multi-objective evolution of neural network classifier ensembles from imbalanced data.

For the last decade it has become commonplace to evaluate machine learning techniques for network based intrusion detection on the KDD Cup '99 data set. This data set has served well to demonstrate that machine learning can be useful in intrusion detection. However, it has undergone some criticism in the literature, and it is out of date. Therefore, some researchers question the validity of the findings reported based on this data set. Furthermore, as identified in this thesis, there are also discrepancies in the findings reported in the literature. In some cases the results are contradictory. Consequently, it is difficult to analyse the current body of research to determine the value in the findings. This thesis reports on an empirical investigation to determine the underlying causes of the discrepancies. Several methodological factors, such as choice of data subset, validation method and data preprocessing, are identified and are found to affect the results significantly. These findings have also enabled a better interpretation of the current body of research. Furthermore, the criticisms in the literature are addressed and future use of the data set is discussed, which is important since researchers continue to use it due to a lack of better publicly available alternatives. Due to the nature of the intrusion detection domain, there is an extreme imbalance among the classes in the KDD Cup '99 data set, which poses a significant challenge to machine learning. In other domains, researchers have demonstrated that well known techniques such as Artificial Neural Networks (ANNs) and Decision Trees (DTs) often fail to learn the minor class(es) due to class imbalance. However, this has not been recognized as an issue in intrusion detection previously. This thesis reports on an empirical investigation that demonstrates that it is the class imbalance that causes the poor detection of some classes of intrusion reported in the literature. An alternative approach to training ANNs is proposed in this thesis, using Genetic Algorithms (GAs) to evolve the weights of the ANNs, referred to as an Evolutionary Neural Network (ENN). When employing evaluation functions that calculate the fitness proportionally to the instances of each class, thereby avoiding a bias towards the major class(es) in the data set, significantly improved true positive rates are obtained whilst maintaining a low false positive rate. These findings demonstrate that the issues of learning from imbalanced data are not due to limitations of the ANNs; rather the training algorithm. Moreover, the ENN is capable of detecting a class of intrusion that has been reported in the literature to be undetectable by ANNs. One limitation of the ENN is a lack of control of the classification trade-off the ANNs obtain. This is identified as a general issue with current approaches to creating classifiers. Striving to create a single best classifier that obtains the highest accuracy may give an unfruitful classification trade-off, which is demonstrated clearly in this thesis. Therefore, an extension of the ENN is proposed, using a Multi-Objective GA (MOGA), which treats the classification rate on each class as a separate objective. This approach produces a Pareto front of non-dominated solutions that exhibit different classification trade-offs, from which the user can select one with the desired properties. The multi-objective approach is also utilised to evolve classifier ensembles, which yields an improved Pareto front of solutions. Furthermore, the selection of classifier members for the ensembles is investigated, demonstrating how this affects the performance of the resultant ensembles. This is a key to explaining why some classifier combinations fail to give fruitful solutions

Text processing using neural networks

Natural language processing is a key technology in the field of artificial intelligence. It involves the two basic tasks of natural language understanding and natural language generation. The primary core of solving the above tasks is to obtain text semantics. Text semantic analysis enables computers to simulate humans to understand the deep semantics of natural language and identify the true meaning contained in information by building a model. Obtaining the true semantics of text helps to improve the processing effect of various natural language processing downstream tasks, such as machine translation, question answering systems, and chatbots. Natural language text is composed of words, sentences and paragraphs (in that order). Word-level semantic analysis is concerned with the sense of words, the quality of which directly affects the quality of subsequent text semantics at each level. Sentences are the simplest sequence of semantic units, and sentence-level semantics analysis focuses on the semantics expressed by the entire sentence. Paragraph semantic analysis achieves the purpose of understanding paragraph semantics. Currently, while the performance of semantic analysis models based on Deep Neural Network has made significant progress, many shortcomings remain. This thesis proposes the Deep Neural Network-based model for sentence semantic understanding, word sense understanding and text sequence generation from the perspective of different research tasks to address the difficulties in text semantic analysis. The research contents and contributions are summarized as follows: First, the mainstream use of recurrent neural networks cannot directly model the latent structural information of sentences. To better determine the sense of ambiguous words, this thesis proposes a model that uses a two-layer bi-directional long short-term memory neural network and attention mechanism. Second, static word embedding models cannot manage polysemy. Contextual word embedding models can do so, however, their performance is limited in application scenarios with high real-time requirements. Accordingly, this thesis proposes using a word sense induction task to construct word sense embeddings for polysemous words. Third, the current mainstream encoder-decoder model based on the attention mechanism does not explicitly perform a preliminary screening of the information in the source text before summary generation. This results in the input to the decoder containing a large amount of information irrelevant to summary generation as well as exposure bias and out-of-vocabulary words in the generation of sequences. To address this problem, this thesis proposes an abstractive text summarization model based on a hierarchical attention mechanism and multi-objective reinforcement learning. In summary, this thesis conducts in-depth research on semantic analysis, and proposes solutions to problems in word sense disambiguation, word sense embeddings, and abstractive text summarization tasks. The feasibility and validity were verified through extensive experiments on their respective corresponding publicly-available standard datasets, and also provide support for other related research in the field of natural language processing.Natural language processing is a key technology in the field of artificial intelligence. It involves the two basic tasks of natural language understanding and natural language generation. The primary core of solving the above tasks is to obtain text semantics. Text semantic analysis enables computers to simulate humans to understand the deep semantics of natural language and identify the true meaning contained in information by building a model. Obtaining the true semantics of text helps to improve the processing effect of various natural language processing downstream tasks, such as machine translation, question answering systems, and chatbots. Natural language text is composed of words, sentences and paragraphs (in that order). Word-level semantic analysis is concerned with the sense of words, the quality of which directly affects the quality of subsequent text semantics at each level. Sentences are the simplest sequence of semantic units, and sentence-level semantics analysis focuses on the semantics expressed by the entire sentence. Paragraph semantic analysis achieves the purpose of understanding paragraph semantics. Currently, while the performance of semantic analysis models based on Deep Neural Network has made significant progress, many shortcomings remain. This thesis proposes the Deep Neural Network-based model for sentence semantic understanding, word sense understanding and text sequence generation from the perspective of different research tasks to address the difficulties in text semantic analysis. The research contents and contributions are summarized as follows: First, the mainstream use of recurrent neural networks cannot directly model the latent structural information of sentences. To better determine the sense of ambiguous words, this thesis proposes a model that uses a two-layer bi-directional long short-term memory neural network and attention mechanism. Second, static word embedding models cannot manage polysemy. Contextual word embedding models can do so, however, their performance is limited in application scenarios with high real-time requirements. Accordingly, this thesis proposes using a word sense induction task to construct word sense embeddings for polysemous words. Third, the current mainstream encoder-decoder model based on the attention mechanism does not explicitly perform a preliminary screening of the information in the source text before summary generation. This results in the input to the decoder containing a large amount of information irrelevant to summary generation as well as exposure bias and out-of-vocabulary words in the generation of sequences. To address this problem, this thesis proposes an abstractive text summarization model based on a hierarchical attention mechanism and multi-objective reinforcement learning. In summary, this thesis conducts in-depth research on semantic analysis, and proposes solutions to problems in word sense disambiguation, word sense embeddings, and abstractive text summarization tasks. The feasibility and validity were verified through extensive experiments on their respective corresponding publicly-available standard datasets, and also provide support for other related research in the field of natural language processing.460 - Katedra informatikyvyhově

Machine learning for network based intrusion detection : an investigation into discrepancies in findings with the KDD cup '99 data set and multi-objective evolution of neural network classifier ensembles from imbalanced data

For the last decade it has become commonplace to evaluate machine learning techniques for network based intrusion detection on the KDD Cup '99 data set. This data set has served well to demonstrate that machine learning can be useful in intrusion detection. However, it has undergone some criticism in the literature, and it is out of date. Therefore, some researchers question the validity of the findings reported based on this data set. Furthermore, as identified in this thesis, there are also discrepancies in the findings reported in the literature. In some cases the results are contradictory. Consequently, it is difficult to analyse the current body of research to determine the value in the findings. This thesis reports on an empirical investigation to determine the underlying causes of the discrepancies. Several methodological factors, such as choice of data subset, validation method and data preprocessing, are identified and are found to affect the results significantly. These findings have also enabled a better interpretation of the current body of research. Furthermore, the criticisms in the literature are addressed and future use of the data set is discussed, which is important since researchers continue to use it due to a lack of better publicly available alternatives. Due to the nature of the intrusion detection domain, there is an extreme imbalance among the classes in the KDD Cup '99 data set, which poses a significant challenge to machine learning. In other domains, researchers have demonstrated that well known techniques such as Artificial Neural Networks (ANNs) and Decision Trees (DTs) often fail to learn the minor class(es) due to class imbalance. However, this has not been recognized as an issue in intrusion detection previously. This thesis reports on an empirical investigation that demonstrates that it is the class imbalance that causes the poor detection of some classes of intrusion reported in the literature. An alternative approach to training ANNs is proposed in this thesis, using Genetic Algorithms (GAs) to evolve the weights of the ANNs, referred to as an Evolutionary Neural Network (ENN). When employing evaluation functions that calculate the fitness proportionally to the instances of each class, thereby avoiding a bias towards the major class(es) in the data set, significantly improved true positive rates are obtained whilst maintaining a low false positive rate. These findings demonstrate that the issues of learning from imbalanced data are not due to limitations of the ANNs; rather the training algorithm. Moreover, the ENN is capable of detecting a class of intrusion that has been reported in the literature to be undetectable by ANNs. One limitation of the ENN is a lack of control of the classification trade-off the ANNs obtain. This is identified as a general issue with current approaches to creating classifiers. Striving to create a single best classifier that obtains the highest accuracy may give an unfruitful classification trade-off, which is demonstrated clearly in this thesis. Therefore, an extension of the ENN is proposed, using a Multi-Objective GA (MOGA), which treats the classification rate on each class as a separate objective. This approach produces a Pareto front of non-dominated solutions that exhibit different classification trade-offs, from which the user can select one with the desired properties. The multi-objective approach is also utilised to evolve classifier ensembles, which yields an improved Pareto front of solutions. Furthermore, the selection of classifier members for the ensembles is investigated, demonstrating how this affects the performance of the resultant ensembles. This is a key to explaining why some classifier combinations fail to give fruitful solutions.EThOS - Electronic Theses Online ServiceGBUnited Kingdo