Abstract: Bus networks design is an important problem in
public transportation. The main step to this design, is determining the
number of required terminals and their locations. This is an especial
type of facility location problem, a large scale combinatorial
optimization problem that requires a long time to be solved.
The genetic algorithm (GA) is a search and optimization technique
which works based on evolutionary principle of natural
chromosomes. Specifically, the evolution of chromosomes due to the
action of crossover, mutation and natural selection of chromosomes
based on Darwin's survival-of-the-fittest principle, are all artificially
simulated to constitute a robust search and optimization procedure.
In this paper, we first state the problem as a mixed integer
programming (MIP) problem. Then we design a new crossover and
mutation for bus terminal location problem (BTLP). We tested the
different parameters of genetic algorithm (for a sample problem) and
obtained the optimal parameters for solving BTLP with numerical try
and error.
Abstract: There are multiple reasons to expect that detecting the
word order errors in a text will be a difficult problem, and detection
rates reported in the literature are in fact low. Although grammatical
rules constructed by computer linguists improve the performance of
grammar checker in word order diagnosis, the repairing task is still
very difficult. This paper presents an approach for repairing word
order errors in English text by reordering words in a sentence and
choosing the version that maximizes the number of trigram hits
according to a language model. The novelty of this method concerns
the use of an efficient confusion matrix technique for reordering the
words. The comparative advantage of this method is that works with
a large set of words, and avoids the laborious and costly process of
collecting word order errors for creating error patterns.
Abstract: A new secure knapsack cryptosystem based on the
Merkle-Hellman public key cryptosystem will be proposed in this
paper. Although it is common sense that when the density is low, the
knapsack cryptosystem turns vulnerable to the low-density attack. The
density d of a secure knapsack cryptosystem must be larger than
0.9408 to avoid low-density attack. In this paper, we investigate a
new Permutation Combination Algorithm. By exploiting this
algorithm, we shall propose a novel knapsack public-key cryptosystem.
Our proposed scheme can enjoy a high density to avoid the
low-density attack. The density d can also exceed 0.9408 to avoid
the low-density attack.
Abstract: Evolutionary robotics is concerned with the design of
intelligent systems with life-like properties by means of simulated
evolution. Approaches in evolutionary robotics can be categorized
according to the control structures that represent the behavior and the
parameters of the controller that undergo adaptation. The basic idea
is to automatically synthesize behaviors that enable the robot to
perform useful tasks in complex environments. The evolutionary
algorithm searches through the space of parameterized controllers
that map sensory perceptions to control actions, thus realizing a
specific robotic behavior. Further, the evolutionary algorithm
maintains and improves a population of candidate behaviors by
means of selection, recombination and mutation. A fitness function
evaluates the performance of the resulting behavior according to the
robot-s task or mission. In this paper, the focus is in the use of
genetic algorithms to solve a multi-objective optimization problem
representing robot behaviors; in particular, the A-Compander Law is
employed in selecting the weight of each objective during the
optimization process. Results using an adaptive fitness function show
that this approach can efficiently react to complex tasks under
variable environments.
Abstract: Bioinformatics and computational biology involve
the use of techniques including applied mathematics,
informatics, statistics, computer science, artificial intelligence,
chemistry, and biochemistry to solve biological problems
usually on the molecular level. Research in computational
biology often overlaps with systems biology. Major research
efforts in the field include sequence alignment, gene finding,
genome assembly, protein structure alignment, protein structure
prediction, prediction of gene expression and proteinprotein
interactions, and the modeling of evolution. Various
global rearrangements of permutations, such as reversals and
transpositions,have recently become of interest because of their
applications in computational molecular biology. A reversal is
an operation that reverses the order of a substring of a permutation.
A transposition is an operation that swaps two adjacent
substrings of a permutation. The problem of determining the
smallest number of reversals required to transform a given
permutation into the identity permutation is called sorting by
reversals. Similar problems can be defined for transpositions
and other global rearrangements. In this work we perform a
study about some genome rearrangement primitives. We show
how a genome is modelled by a permutation, introduce some
of the existing primitives and the lower and upper bounds
on them. We then provide a comparison of the introduced
primitives.
Abstract: This paper presents a formalisation of the different existing code mutation techniques (polymorphism and metamorphism) by means of formal grammars. While very few theoretical results are known about the detection complexity of viral mutation techniques, we exhaustively address this critical issue by considering the Chomsky classification of formal grammars. This enables us to determine which family of code mutation techniques are likely to be detected or on the contrary are bound to remain undetected. As an illustration we then present, on a formal basis, a proof-of-concept metamorphic mutation engine denoted PB MOT, whose detection has been proven to be undecidable.
Abstract: Evolutionary Programming (EP) represents a
methodology of Evolutionary Algorithms (EA) in which mutation is
considered as a main reproduction operator. This paper presents a
novel EP approach for Artificial Neural Networks (ANN) learning.
The proposed strategy consists of two components: the self-adaptive,
which contains phenotype information and the dynamic, which is
described by genotype. Self-adaptation is achieved by the addition of
a value, called the network weight, which depends on a total number
of hidden layers and an average number of neurons in hidden layers.
The dynamic component changes its value depending on the fitness
of a chromosome, exposed to mutation. Thus, the mutation step size
is controlled by two components, encapsulated in the algorithm,
which adjust it according to the characteristics of a predefined ANN
architecture and the fitness of a particular chromosome. The
comparative analysis of the proposed approach and the classical EP
(Gaussian mutation) showed, that that the significant acceleration of
the evolution process is achieved by using both phenotype and
genotype information in the mutation strategy.
Abstract: The ability to distinguish missense nucleotide
substitutions that contribute to harmful effect from those that do not
is a difficult problem usually accomplished through functional in
vivo analyses. In this study, instead current biochemical methods, the
effects of missense mutations upon protein structure and function
were assayed by means of computational methods and information
from the databases. For this order, the effects of new missense
mutations in exon 5 of PTEN gene upon protein structure and
function were examined. The gene coding for PTEN was identified
and localized on chromosome region 10q23.3 as the tumor
suppressor gene. The utilization of these methods were shown that
c.319G>A and c.341T>G missense mutations that were recognized in
patients with breast cancer and Cowden disease, could be pathogenic.
This method could be use for analysis of missense mutation in others
genes.
Abstract: The aim of this paper is to express the input-output
matrix as a linear ordering problem which is classified as an NP-hard
problem. We then use a Tabu search algorithm to find the best
permutation among sectors in the input-output matrix that will give
an optimal solution. This optimal permutation can be useful in
designing policies and strategies for economists and government in
their goal of maximizing the gross domestic product.
Abstract: Brand name plays a vital role for in-shop buying
behavior of consumers and mutated brand name may affect the
selling of leading branded products. In Indian market, there are many
products with mutated brand names which are either orthographically
or phonologically similar. Due to presence of such products, Indian
consumers very often fall under confusion when buying some
regularly used stuff. Authors of the present paper have attempted to
demonstrate relationship between less attention and false recognition
of mutated brand names during a product selection process. To
achieve this goal, visual attention study was conducted on 15 male
college students using eye-tracker against a mutated brand name and
errors in recognition were noted using questionnaire. Statistical
analysis of the acquired data revealed that there was more false
recognition of mutated brand name when less attention was paid
during selection of favorite product. Moreover, it was perceived that
eye tracking is an effective tool for analyzing false recognition of
brand name mutation.
Abstract: Spatial trends are one of the valuable patterns in geo
databases. They play an important role in data analysis and
knowledge discovery from spatial data. A spatial trend is a regular
change of one or more non spatial attributes when spatially moving
away from a start object. Spatial trend detection is a graph search
problem therefore heuristic methods can be good solution. Artificial
immune system (AIS) is a special method for searching and
optimizing. AIS is a novel evolutionary paradigm inspired by the
biological immune system. The models based on immune system
principles, such as the clonal selection theory, the immune network
model or the negative selection algorithm, have been finding
increasing applications in fields of science and engineering.
In this paper, we develop a novel immunological algorithm based
on clonal selection algorithm (CSA) for spatial trend detection. We
are created neighborhood graph and neighborhood path, then select
spatial trends that their affinity is high for antibody. In an
evolutionary process with artificial immune algorithm, affinity of
low trends is increased with mutation until stop condition is satisfied.
Abstract: A systems approach model for prostate cancer in prostate duct, as a sub-system of the organism is developed. It is accomplished in two steps. First this research work starts with a nonlinear system of coupled Fokker-Plank equations which models continuous process of the system like motion of cells. Then extended to PDEs that include discontinuous processes like cell mutations, proliferation and deaths. The discontinuous processes is modeled by using intensity poisson processes. The model incorporates the features of the prostate duct. The system of PDEs spatial coordinate is along the proximal distal axis. Its parameters depend on features of the prostate duct. The movement of cells is biased towards distal region and mutations of prostate cancer cells is localized in the proximal region. Numerical solutions of the full system of equations are provided, and are exhibit traveling wave fronts phenomena. This motivates the use of the standard transformation to derive a canonically related system of ODEs for traveling wave solutions. The results obtained show persistence of prostate cancer by showing that the non-negative cone for the traveling wave system is time invariant. The traveling waves have a unique global attractor is proved also. Biologically, the global attractor verifies that evolution of prostate cancer stem cells exhibit the avascular tumor growth. These numerical solutions show that altering prostate stem cell movement or mutation of prostate cancer cells lead to avascular tumor. Conclusion with comments on clinical implications of the model is discussed.
Abstract: Fourty one strains of ESBL producing P.aeruginosa
which were previously isolated from burn patients in Kerman
University general hospital, Iran were subjected to PCR, RFLP and
sequencing in order to determine the type of extended spectrum β-
lactamases (ESBL), the restriction digestion pattern and possibility of
mutation among detected genes. DNA extraction was carried out by
phenol chloroform method. PCR for detection of bla genes was
performed using specific primer for each gene. Restriction Fragment
Length Polymorphism (RFLP) for ESBL genes was carried out using
EcoRI, NheI, PVUII, EcoRV, DdeI, and PstI restriction enzymes. The
PCR products were subjected to direct sequencing of both the strands
for identification of the ESBL genes.The blaCTX-M, blaVEB-1, blaPER-1,
blaGES-1, blaOXA-1, blaOXA-4 and blaOXA-10 genes were detected in the
(n=1) 2.43%, (n=41)100%, (n=28) 68.3%, (n=10) 24.4%, (n=29)
70.7%, (n=7)17.1% and (n=38) 92.7% of the ESBL producing isolates
respectively. The RFLP analysis showed that each ESBL gene has
identical pattern of digestion among the isolated strains. Sequencing
of the ESBL genes confirmed the genuinety of PCR products and
revealed no mutation in the restriction sites of the above genes. From
results of the present investigation it can be concluded that blaVEB-1
and blaCTX-M were the most and the least frequently isolated ESBL
genes among the P.aeruginosa strains isolated from burn patients. The
RFLP and sequencing analysis revealed that same clone of the bla
genes were indeed existed among the antibiotic resistant strains.