1,072 research outputs found
Network intrusion detection using genetic programming.
Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.Network intrusion detection is a real-world problem that involves detecting intrusions on a computer network. Detecting whether a network connection is intrusive or non-intrusive is essentially a binary classification problem. However, the type of intrusive connections can be categorised into a number of network attack classes and the task of associating an intrusion to a particular network type is multiclass classification.
A number of artificial intelligence techniques have been used for network intrusion detection including Evolutionary Algorithms. This thesis investigates the application of evolutionary algorithms namely, Genetic Programming (GP), Grammatical Evolution (GE) and Multi-Expression Programming (MEP) in the network intrusion detection domain. Grammatical evolution and multi-expression programming are considered to be variants of GP. In this thesis, a comparison of the effectiveness of classifiers evolved by the three EAs within the network intrusion detection domain is performed. The comparison is performed on the publicly available KDD99 dataset. Furthermore, the effectiveness of a number of fitness functions is evaluated.
From the results obtained, standard genetic programming performs better than grammatical evolution and multi-expression programming. The findings indicate that binary classifiers evolved using standard genetic programming outperformed classifiers evolved using grammatical evolution and multi-expression programming. For evolving multiclass classifiers different fitness functions used produced classifiers with different characteristics resulting in some classifiers achieving higher detection rates for specific network intrusion attacks as compared to other intrusion attacks. The findings indicate that classifiers evolved using multi-expression programming and genetic programming achieved high detection rates as compared to classifiers evolved using grammatical evolution
Towards the Evolution of Novel Vertical-Axis Wind Turbines
Renewable and sustainable energy is one of the most important challenges
currently facing mankind. Wind has made an increasing contribution to the
world's energy supply mix, but still remains a long way from reaching its full
potential. In this paper, we investigate the use of artificial evolution to
design vertical-axis wind turbine prototypes that are physically instantiated
and evaluated under approximated wind tunnel conditions. An artificial neural
network is used as a surrogate model to assist learning and found to reduce the
number of fabrications required to reach a higher aerodynamic efficiency,
resulting in an important cost reduction. Unlike in other approaches, such as
computational fluid dynamics simulations, no mathematical formulations are used
and no model assumptions are made.Comment: 14 pages, 11 figure
Neural networks for genetic epidemiology: past, present, and future
During the past two decades, the field of human genetics has experienced an information explosion. The completion of the human genome project and the development of high throughput SNP technologies have created a wealth of data; however, the analysis and interpretation of these data have created a research bottleneck. While technology facilitates the measurement of hundreds or thousands of genes, statistical and computational methodologies are lacking for the analysis of these data. New statistical methods and variable selection strategies must be explored for identifying disease susceptibility genes for common, complex diseases. Neural networks (NN) are a class of pattern recognition methods that have been successfully implemented for data mining and prediction in a variety of fields. The application of NN for statistical genetics studies is an active area of research. Neural networks have been applied in both linkage and association analysis for the identification of disease susceptibility genes
Biomimetic Engineering
Humankind is a privileged animal species for many reasons. A remarkable one is its
ability to conceive and manufacture objects. Human industry is indeed leading the
various winning strategies (along with language and culture) that has permitted this
primate to extraordinarily increase its life expectancy and proliferation rate. (It is indeed
so successful, that it now threatens the whole planet.) The design of this industry kicks
off in the brain, a computing machine particularly good at storing, recognizing and
associating patterns. Even in a time when human beings tend to populate non-natural,
man-made environments, the many forms, colorings, textures and behaviors of nature
continuously excite our senses and blend in our thoughts, even more deeply during
childhood. Then, it would be exaggerated to say that Biomimetics is a brand new
strategy. As long as human creation is based on previously acquired knowledge and
experiences, it is not surprising that engineering, the arts, and any form of expression, is
influenced by nature’s way to some extent.
The design of human industry has evolved from very simple tools, to complex
engineering devices. Nature has always provided us with a rich catalog of excellent
materials and inspiring designs. Now, equipped with new machinery and techniques, we
look again at Nature. We aim at mimicking not only its best products, but also its design
principles.
Organic life, as we know it, is indeed a vast pool of diversity. Living matter inhabits
almost every corner of the terrestrial ecosphere. From warm open-air ecosystems to the
extreme conditions of hot salt ponds, living cells have found ways to metabolize the
sources of energy, and get organized in complex organisms of specialized tissues and organs that adapt themselves to the environment, and can modify the environment to
their own needs as well. Life on Earth has evolved such a diverse portfolio of species
that the number of designs, mechanisms and strategies that can actually be abstracted is
astonishing. As August Krogh put it: "For a large number of problems there will be
some animal of choice, on which it can be most conveniently studied".
The scientific method starts with a meticulous observation of natural phenomena, and
humans are particularly good at that game. In principle, the aim of science is to
understand the physical world, but an observer’s mind can behave either as an engineer
or as a scientist. The minute examination of the many living forms that surround us has
led to the understanding of new organizational principles, some of which can be
imported in our production processes. In practice, bio-inspiration can arise at very
different levels of observation: be it social organization, the shape of an organism, the
structure and functioning of organs, tissular composition, cellular form and behavior, or
the detailed structure of molecules. Our direct experience of the wide portfolio of
species found in nature, and their particular organs, have clearly favored that the initial
models would come from the organism and organ levels. But the development of new
techniques (on one hand to observe the micro- and nanostructure of living beings, and
on the other to simulate the complex behavior of social communities) have significantly
extended the domain of interest
Risk Management using Model Predictive Control
Forward planning and risk management are crucial for the success of any system or business dealing with the uncertainties of the real world. Previous approaches have largely assumed that the future will be similar to the past, or used simple forecasting techniques based on ad-hoc models. Improving solutions requires better projection of future events, and necessitates robust forward planning techniques that consider forecasting inaccuracies. This work advocates risk management through optimal control theory, and proposes several techniques to combine it with time-series forecasting. Focusing on applications in foreign exchange (FX) and battery energy storage systems (BESS), the contributions of this thesis are three-fold. First, a short-term risk management system for FX dealers is formulated as a stochastic model predictive control (SMPC) problem in which the optimal risk-cost profiles are obtained through dynamic control of the dealers’ positions on the spot market. Second, grammatical evolution (GE) is used to automate non-linear time-series model selection, validation, and forecasting. Third, a novel measure for evaluating forecasting models, as a part of the predictive model in finite horizon optimal control applications, is proposed. Using both synthetic and historical data, the proposed techniques were validated and benchmarked. It was shown that the stochastic FX risk management system exhibits better risk management on a risk-cost Pareto frontier compared to rule-based hedging strategies, with up to 44.7% lower cost for the same level of risk. Similarly, for a real-world BESS application, it was demonstrated that the GE optimised forecasting models outperformed other prediction models by at least 9%, improving the overall peak shaving capacity of the system to 57.6%
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Proceedings of ECAI International Workshop on Neural-Symbolic Learning and reasoning NeSy 2006
Advances in Computer Science and Engineering
The book Advances in Computer Science and Engineering constitutes the revised selection of 23 chapters written by scientists and researchers from all over the world. The chapters cover topics in the scientific fields of Applied Computing Techniques, Innovations in Mechanical Engineering, Electrical Engineering and Applications and Advances in Applied Modeling
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