Abstract: Every 2-3 years the influenza B virus serves
epidemics. Neuraminidase (NA) is an important target for influenza
drug design. Although, oseltamivir, an oral neuraminidase drug, has
been shown good inhibitory efficiency against wild-type of influenza
B virus, the lower susceptibility to the R152K mutation has been
reported. Better understanding of oseltamivir efficiency and
resistance toward the influenza B NA wild-type and R152K mutant,
respectively, could be useful for rational drug design. Here, two
complex systems of wild-type and R152K NAs with oseltamivir
bound were studied using molecular dynamics (MD) simulations.
Based on 5-ns MD simulation, the loss of notable hydrogen bond and
decrease in per-residue decomposition energy from the mutated
residue K152 contributed to drug compared to those of R152 in wildtype
were found to be a primary source of high-level of oseltamivir
resistance due to the R152K mutation.
Abstract: In this paper we have proposed a novel dynamic least cost multicast routing protocol using hybrid genetic algorithm for IP networks. Our protocol finds the multicast tree with minimum cost subject to delay, degree, and bandwidth constraints. The proposed protocol has the following features: i. Heuristic local search function has been devised and embedded with normal genetic operation to increase the speed and to get the optimized tree, ii. It is efficient to handle the dynamic situation arises due to either change in the multicast group membership or node / link failure, iii. Two different crossover and mutation probabilities have been used for maintaining the diversity of solution and quick convergence. The simulation results have shown that our proposed protocol generates dynamic multicast tree with lower cost. Results have also shown that the proposed algorithm has better convergence rate, better dynamic request success rate and less execution time than other existing algorithms. Effects of degree and delay constraints have also been analyzed for the multicast tree interns of search success rate.
Abstract: Society has grown to rely on Internet services, and the
number of Internet users increases every day. As more and more
users become connected to the network, the window of opportunity
for malicious users to do their damage becomes very great and
lucrative. The objective of this paper is to incorporate different
techniques into classier system to detect and classify intrusion from
normal network packet. Among several techniques, Steady State
Genetic-based Machine Leaning Algorithm (SSGBML) will be used
to detect intrusions. Where Steady State Genetic Algorithm (SSGA),
Simple Genetic Algorithm (SGA), Modified Genetic Algorithm and
Zeroth Level Classifier system are investigated in this research.
SSGA is used as a discovery mechanism instead of SGA. SGA
replaces all old rules with new produced rule preventing old good
rules from participating in the next rule generation. Zeroth Level
Classifier System is used to play the role of detector by matching
incoming environment message with classifiers to determine whether
the current message is normal or intrusion and receiving feedback
from environment. Finally, in order to attain the best results,
Modified SSGA will enhance our discovery engine by using Fuzzy
Logic to optimize crossover and mutation probability. The
experiments and evaluations of the proposed method were performed
with the KDD 99 intrusion detection dataset.
Abstract: The control of commutation of switched reluctance
(SR) motor has nominally depended on a physical position detector.
The physical rotor position sensor limits robustness and increases
size and inertia of the SR drive system. The paper describes a method
to overcome these limitations by using magnetization characteristics
of the motor to indicate rotor and stator teeth overlap status. The
method is using active current probing pulses of same magnitude that
is used to simulate flux linkage in the winding being probed. A
microprocessor is used for processing magnetization data to deduce
rotor-stator teeth overlap status and hence rotor position. However,
the back-of-core saturation and mutual coupling introduces overlap
detection errors, hence that of commutation control. This paper
presents the concept of the detection scheme and the effects of backof
core saturation.
Abstract: In this paper, a novel associative memory model will be proposed and applied to memory retrievals based on the conventional continuous time model. The conventional model presents memory capacity is very low and retrieval process easily converges to an equilibrium state which is very different from the stored patterns. Genetic Algorithms is well-known with the capability of global optimal search escaping local optimum on progress to reach a global optimum. Based on the well-known idea of Genetic Algorithms, this work proposes a heuristic rule to make a mutation when the state of the network is trapped in a spurious memory. The proposal heuristic associative memory show the stored capacity does not depend on the number of stored patterns and the retrieval ability is up to ~ 1.
Abstract: Adaptive Genetic Algorithms extend the Standard Gas
to use dynamic procedures to apply evolutionary operators such as
crossover, mutation and selection. In this paper, we try to propose a
new adaptive genetic algorithm, which is based on the statistical
information of the population as a guideline to tune its crossover,
selection and mutation operators. This algorithms is called Statistical
Genetic Algorithm and is compared with traditional GA in some
benchmark problems.
Abstract: The Block Sorting problem is to sort a given
permutation moving blocks. A block is defined as a substring
of the given permutation, which is also a substring of the
identity permutation. Block Sorting has been proved to be
NP-Hard. Until now two different 2-Approximation algorithms
have been presented for block sorting. These are the best known
algorithms for Block Sorting till date. In this work we present
a different characterization of Block Sorting in terms of a
transposition cycle graph. Then we suggest a heuristic,
which we show to exhibit a 2-approximation performance
guarantee for most permutations.
Abstract: The crossed cube is one of the most notable variations of hypercube, but some properties of the former are superior to those of the latter. For example, the diameter of the crossed cube is almost the half of that of the hypercube. In this paper, we focus on the problem embedding a Hamiltonian cycle through an arbitrary given edge in the crossed cube. We give necessary and sufficient condition for determining whether a given permutation with n elements over Zn generates a Hamiltonian cycle pattern of the crossed cube. Moreover, we obtain a lower bound for the number of different Hamiltonian cycles passing through a given edge in an n-dimensional crossed cube. Our work extends some recently obtained results.
Abstract: This study focuses on examining why the range of
experience with respect to HIV infection is so diverse, especially in
regard to the latency period. An agent-based approach in modelling
the infection is used to extract high-level behaviour which cannot be
obtained analytically from the set of interaction rules at the cellular
level. A prototype model encompasses local variation in baseline
properties, contributing to the individual disease experience, and is
included in a network which mimics the chain of lymph nodes. The
model also accounts for stochastic events such as viral mutations.
The size and complexity of the model require major computational
effort and parallelisation methods are used.
Abstract: Independent component analysis (ICA) in the
frequency domain is used for solving the problem of blind source
separation (BSS). However, this method has some problems. For
example, a general ICA algorithm cannot determine the permutation
of signals which is important in the frequency domain ICA. In this
paper, we propose an approach to the solution for a permutation
problem. The idea is to effectively combine two conventional
approaches. This approach improves the signal separation
performance by exploiting features of the conventional approaches.
We show the simulation results using artificial data.
Abstract: Multiprocessor task scheduling is a NP-hard problem and Genetic Algorithm (GA) has been revealed as an excellent technique for finding an optimal solution. In the past, several methods have been considered for the solution of this problem based on GAs. But, all these methods consider single criteria and in the present work, minimization of the bi-criteria multiprocessor task scheduling problem has been considered which includes weighted sum of makespan & total completion time. Efficiency and effectiveness of genetic algorithm can be achieved by optimization of its different parameters such as crossover, mutation, crossover probability, selection function etc. The effects of GA parameters on minimization of bi-criteria fitness function and subsequent setting of parameters have been accomplished by central composite design (CCD) approach of response surface methodology (RSM) of Design of Experiments. The experiments have been performed with different levels of GA parameters and analysis of variance has been performed for significant parameters for minimisation of makespan and total completion time simultaneously.
Abstract: In this paper, we probe into the traffic assignment problem by the chromosome-learning-based path finding method in simulation, which is to model the driver' behavior in the with-in-a-day process. By simply making a combination and a change of the traffic route chromosomes, the driver at the intersection chooses his next route. The various crossover and mutation rules are proposed with extensive examples.
Abstract: Random Forests are a powerful classification technique, consisting of a collection of decision trees. One useful feature of Random Forests is the ability to determine the importance of each variable in predicting the outcome. This is done by permuting each variable and computing the change in prediction accuracy before and after the permutation. This variable importance calculation is similar to a one-factor-at a time experiment and therefore is inefficient. In this paper, we use a regular fractional factorial design to determine which variables to permute. Based on the results of the trials in the experiment, we calculate the individual importance of the variables, with improved precision over the standard method. The method is illustrated with a study of student attrition at Monash University.
Abstract: Optimal reactive power flow is an optimization problem
with one or more objective of minimizing the active power losses for
fixed generation schedule. The control variables are generator bus
voltages, transformer tap settings and reactive power output of the
compensating devices placed on different bus bars. Biogeography-
Based Optimization (BBO) technique has been applied to solve
different kinds of optimal reactive power flow problems subject
to operational constraints like power balance constraint, line flow
and bus voltages limits etc. BBO searches for the global optimum
mainly through two steps: Migration and Mutation. In the present
work, BBO has been applied to solve the optimal reactive power
flow problems on IEEE 30-bus and standard IEEE 57-bus power
systems for minimization of active power loss. The superiority of the
proposed method has been demonstrated. Considering the quality of
the solution obtained, the proposed method seems to be a promising
one for solving these problems.
Abstract: The PAX6, a transcription factor, is essential for the morphogenesis of the eyes, brain, pituitary and pancreatic islets. In rodents, the loss of Pax6 function leads to central nervous system defects, anophthalmia, and nasal hypoplasia. The haplo-insufficiency of Pax6 causes microphthalmia, aggression and other behavioral abnormalities. It is also required in brain patterning and neuronal plasticity. In human, heterozygous mutation of Pax6 causes loss of iris [aniridia], mental retardation and glucose intolerance. The 3- deletion in Pax6 leads to autism and aniridia. The phenotypes are variable in peneterance and expressivity. However, mechanism of function and interaction of PAX6 with other proteins during development and associated disease are not clear. It is intended to explore interactors of PAX6 to elucidated biology of PAX6 function in the tissues where it is expressed and also in the central regulatory pathway. This report describes In-silico approaches to explore interacting proteins of PAX6. The models show several possible proteins interacting with PAX6 like MITF, SIX3, SOX2, SOX3, IPO13, TRIM, and OGT. Since the Pax6 is a critical transcriptional regulator and master control gene of eye and brain development it might be interacting with other protein involved in morphogenesis [TGIF, TGF, Ras etc]. It is also presumed that matricelluar proteins [SPARC, thrombospondin-1 and osteonectin etc] are likely to interact during transport and processing of PAX6 and are somewhere its cascade. The proteins involved in cell survival and cell proliferation can also not be ignored.
Abstract: This paper presents a novel algorithm of stereo
correspondence with rank transform. In this algorithm we used the
genetic algorithm to achieve the accurate disparity map. Genetic
algorithms are efficient search methods based on principles of
population genetic, i.e. mating, chromosome crossover, gene
mutation, and natural selection. Finally morphology is employed to
remove the errors and discontinuities.
Abstract: The literature reports a large number of approaches for
measuring the similarity between protein sequences. Most of these
approaches estimate this similarity using alignment-based techniques
that do not necessarily yield biologically plausible results, for two
reasons.
First, for the case of non-alignable (i.e., not yet definitively aligned
and biologically approved) sequences such as multi-domain, circular
permutation and tandem repeat protein sequences, alignment-based
approaches do not succeed in producing biologically plausible results.
This is due to the nature of the alignment, which is based on the
matching of subsequences in equivalent positions, while non-alignable
proteins often have similar and conserved domains in non-equivalent
positions.
Second, the alignment-based approaches lead to similarity measures
that depend heavily on the parameters set by the user for the alignment
(e.g., gap penalties and substitution matrices). For easily alignable
protein sequences, it's possible to supply a suitable combination of
input parameters that allows such an approach to yield biologically
plausible results. However, for difficult-to-align protein sequences,
supplying different combinations of input parameters yields different
results. Such variable results create ambiguities and complicate the
similarity measurement task.
To overcome these drawbacks, this paper describes a novel and
effective approach for measuring the similarity between protein
sequences, called SAF for Substitution and Alignment Free. Without
resorting either to the alignment of protein sequences or to substitution
relations between amino acids, SAF is able to efficiently detect the
significant subsequences that best represent the intrinsic properties of
protein sequences, those underlying the chronological dependencies of
structural features and biochemical activities of protein sequences.
Moreover, by using a new efficient subsequence matching scheme,
SAF more efficiently handles protein sequences that contain similar
structural features with significant meaning in chronologically
non-equivalent positions. To show the effectiveness of SAF, extensive
experiments were performed on protein datasets from different
databases, and the results were compared with those obtained by
several mainstream algorithms.
Abstract: MATCH project [1] entitle the development of an
automatic diagnosis system that aims to support treatment of colon
cancer diseases by discovering mutations that occurs to tumour
suppressor genes (TSGs) and contributes to the development of
cancerous tumours. The constitution of the system is based on a)
colon cancer clinical data and b) biological information that will be
derived by data mining techniques from genomic and proteomic
sources The core mining module will consist of the popular, well
tested hybrid feature extraction methods, and new combined
algorithms, designed especially for the project. Elements of rough
sets, evolutionary computing, cluster analysis, self-organization maps
and association rules will be used to discover the annotations
between genes, and their influence on tumours [2]-[11].
The methods used to process the data have to address their high
complexity, potential inconsistency and problems of dealing with the
missing values. They must integrate all the useful information
necessary to solve the expert's question. For this purpose, the system
has to learn from data, or be able to interactively specify by a domain
specialist, the part of the knowledge structure it needs to answer a
given query. The program should also take into account the
importance/rank of the particular parts of data it analyses, and adjusts
the used algorithms accordingly.
Abstract: Network reconfiguration in distribution system is realized by changing the status of sectionalizing switches to reduce the power loss in the system. This paper presents a new method which applies an artificial bee colony algorithm (ABC) for determining the sectionalizing switch to be operated in order to solve the distribution system loss minimization problem. The ABC algorithm is a new population based metaheuristic approach inspired by intelligent foraging behavior of honeybee swarm. The advantage of ABC algorithm is that it does not require external parameters such as cross over rate and mutation rate as in case of genetic algorithm and differential evolution and it is hard to determine these parameters in prior. The other advantage is that the global search ability in the algorithm is implemented by introducing neighborhood source production mechanism which is a similar to mutation process. To demonstrate the validity of the proposed algorithm, computer simulations are carried out on 14, 33, and 119-bus systems and compared with different approaches available in the literature. The proposed method has outperformed the other methods in terms of the quality of solution and computational efficiency.
Abstract: This paper presents the application of an enhanced
Particle Swarm Optimization (EPSO) combined with Gaussian
Mutation (GM) for solving the Dynamic Economic Dispatch (DED)
problem considering the operating constraints of generators. The
EPSO consists of the standard PSO and a modified heuristic search
approaches. Namely, the ability of the traditional PSO is enhanced
by applying the modified heuristic search approach to prevent the
solutions from violating the constraints. In addition, Gaussian
Mutation is aimed at increasing the diversity of global search, whilst
it also prevents being trapped in suboptimal points during search. To
illustrate its efficiency and effectiveness, the developed EPSO-GM
approach is tested on the 3-unit and 10-unit 24-hour systems
considering valve-point effect. From the experimental results, it can
be concluded that the proposed EPSO-GM provides, the accurate
solution, the efficiency, and the feature of robust computation
compared with other algorithms under consideration.