893 research outputs found
Tracking Extrema in Dynamic Environment using Multi-Swarm Cellular PSO with Local Search
Many real-world phenomena can be modelled as dynamic optimization problems.
In such cases, the environment problem changes dynamically and therefore,
conventional methods are not capable of dealing with such problems. In this
paper, a novel multi-swarm cellular particle swarm optimization algorithm is
proposed by clustering and local search. In the proposed algorithm, the search
space is partitioned into cells, while the particles identify changes in the
search space and form clusters to create sub-swarms. Then a local search is
applied to improve the solutions in the each cell. Simulation results for
static standard benchmarks and dynamic environments show superiority of the
proposed method over other alternative approaches.Comment: 8 pages, 3 figure
Visualization and clustering by 3D cellular automata: Application to unstructured data
Given the limited performance of 2D cellular automata in terms of space when
the number of documents increases and in terms of visualization clusters, our
motivation was to experiment these cellular automata by increasing the size to
view the impact of size on quality of results. The representation of textual
data was carried out by a vector model whose components are derived from the
overall balancing of the used corpus, Term Frequency Inverse Document Frequency
(TF-IDF). The WorldNet thesaurus has been used to address the problem of the
lemmatization of the words because the representation used in this study is
that of the bags of words. Another independent method of the language was used
to represent textual records is that of the n-grams. Several measures of
similarity have been tested. To validate the classification we have used two
measures of assessment based on the recall and precision (f-measure and
entropy). The results are promising and confirm the idea to increase the
dimension to the problem of the spatiality of the classes. The results obtained
in terms of purity class (i.e. the minimum value of entropy) shows that the
number of documents over longer believes the results are better for 3D cellular
automata, which was not obvious to the 2D dimension. In terms of spatial
navigation, cellular automata provide very good 3D performance visualization
than 2D cellular automata.Comment: 10 pages, 8 figure
Power-Aware Hybrid Intrusion Detection System (PHIDS) using Cellular Automata in Wireless AdHoc Networks
Adhoc wireless network with their changing topology and distributed nature
are more prone to intruders. The network monitoring functionality should be in
operation as long as the network exists with nil constraints. The efficiency of
an Intrusion detection system in the case of an adhoc network is not only
determined by its dynamicity in monitoring but also in its flexibility in
utilizing the available power in each of its nodes. In this paper we propose a
hybrid intrusion detection system, based on a power level metric for potential
adhoc hosts, which is used to determine the duration for which a particular
node can support a network monitoring node. Power aware hybrid intrusion
detection system focuses on the available power level in each of the nodes and
determines the network monitors. Power awareness in the network results in
maintaining power for network monitoring, with monitors changing often, since
it is an iterative power optimal solution to identify nodes for distributed
agent based intrusion detection. The advantage that this approach entails is
the inherent flexibility it provides, by means of considering only fewer nodes
for reestablishing network monitors. The detection of intrusions in the network
is done with the help of Cellular Automat CA. The CAs classify a packet routed
through the network either as normal or an intrusion. The use of CAs enable in
the identification of already occurred intrusions as well as new intrusions
Mean-Field Theory of Meta-Learning
We discuss here the mean-field theory for a cellular automata model of
meta-learning. The meta-learning is the process of combining outcomes of
individual learning procedures in order to determine the final decision with
higher accuracy than any single learning method. Our method is constructed from
an ensemble of interacting, learning agents, that acquire and process incoming
information using various types, or different versions of machine learning
algorithms. The abstract learning space, where all agents are located, is
constructed here using a fully connected model that couples all agents with
random strength values. The cellular automata network simulates the higher
level integration of information acquired from the independent learning trials.
The final classification of incoming input data is therefore defined as the
stationary state of the meta-learning system using simple majority rule, yet
the minority clusters that share opposite classification outcome can be
observed in the system. Therefore, the probability of selecting proper class
for a given input data, can be estimated even without the prior knowledge of
its affiliation. The fuzzy logic can be easily introduced into the system, even
if learning agents are build from simple binary classification machine learning
algorithms by calculating the percentage of agreeing agents.Comment: 23 page
An optimised cellular automata model based on adaptive genetic algorithm for urban growth simulation
This paper presents an improved cellular automata (CA) model optimized using an adaptive genetic algorithm (AGA) to simulate the spatiooral process of urban growth. The AGA technique can be used to optimize the transition rules of the CA model defined through conventional methods such as logistic regression approach, resulting in higher simulation efficiency and improved results. Application of the AGA-CA model in Shanghai's Jiading District, Eastern China demonstrates that the model was able to generate reasonable representation of urban growth even with limited input data in defining its transition rules. The research shows that AGA technique can be integrated within a conventional CA based urban simulation model to improve human understanding on urban dynamics
Automatic Finding Trapezoidal Membership Functions in Mining Fuzzy Association Rules Based on Learning Automata
Association rule mining is an important data mining technique used for discovering relationships among all data items. Membership functions have a significant impact on the outcome of the mining association rules. An important challenge in fuzzy association rule mining is finding an appropriate membership functions, which is an optimization issue. In the most relevant studies of fuzzy association rule mining, only triangle membership functions are considered. This study, as the first attempt, used a team of continuous action-set learning automata (CALA) to find both the appropriate number and positions of trapezoidal membership functions (TMFs). The spreads and centers of the TMFs were taken into account as parameters for the research space and a new approach for the establishment of a CALA team to optimize these parameters was introduced. Additionally, to increase the convergence speed of the proposed approach and remove bad shapes of membership functions, a new heuristic approach has been proposed. Experiments on two real data sets showed that the proposed algorithm improves the efficiency of the extracted rules by finding optimized membership functions
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
Landau Theory of Adaptive Integration in Computational Intelligence
Computational Intelligence (CI) is a sub-branch of Artificial Intelligence
paradigm focusing on the study of adaptive mechanisms to enable or facilitate
intelligent behavior in complex and changing environments. There are several
paradigms of CI [like artificial neural networks, evolutionary computations,
swarm intelligence, artificial immune systems, fuzzy systems and many others],
each of these has its origins in biological systems [biological neural systems,
natural Darwinian evolution, social behavior, immune system, interactions of
organisms with their environment]. Most of those paradigms evolved into
separate machine learning (ML) techniques, where probabilistic methods are used
complementary with CI techniques in order to effectively combine elements of
learning, adaptation, evolution and Fuzzy logic to create heuristic algorithms
that are, in some sense, intelligent. The current trend is to develop consensus
techniques, since no single machine learning algorithms is superior to others
in all possible situations. In order to overcome this problem several
meta-approaches were proposed in ML focusing on the integration of results from
different methods into single prediction. We discuss here the Landau theory for
the nonlinear equation that can describe the adaptive integration of
information acquired from an ensemble of independent learning agents. The
influence of each individual agent on other learners is described similarly to
the social impact theory. The final decision outcome for the consensus system
is calculated using majority rule in the stationary limit, yet the minority
solutions can survive inside the majority population as the complex
intermittent clusters of opposite opinion.Comment: 19 page
Unity Attractors Inspired Programmable Cellular Automata and Barnacles Swarm Optimization-Based Energy Efficient Data Communication for Securing IoT
Wireless Sensor Networks (WSNs) is the innovative technology that covers wide range of application that possesses high potential merits such as long-term operation, unmonitored network access, data transmission, and low implementation cost. In this context, Internet of Things (IoT) have evolved as an exciting paradigm with the rapid advancement of cellular mobile networks, near field communications and cloud computing. WSNs potentially interacts with the IoT devices based on the sensing features of web devices and communication technologies in sensors. At this juncture, IoT need to facilitate huge amount of data aggregation with security and disseminate it to the reliable path to make it reach the required base station. In this paper, Unity Attractors Inspired Programmable Cellular Automata and Barnacles Swarm Optimization-Based Energy Efficient Data Communication Mechanism (UAIPCA-BSO) is proposed for Securing data and estimate the optimal path through which it can be forwarded in the IoT environment. In specific, Unity Attractors Inspired Programmable Cellular Automata is adopted for guaranteeing security during the data transmission process. It also aids in determining the optimal path of data transmission based on the merits of Barnacles Swarm Optimization Algorithm (BSOA), such that data is made to reach the base station at the required destination in time. The simulation results of UAIPCA-BSO confirmed minimized end-to-end delay , accuracy and time taken for malicious node detection, compared to the baseline approaches used for comparison
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