Abstract: An accurate optimal design of laminated composite
structures may present considerable difficulties due to the complexity
and multi-modality of the functional design space. The Big Bang
– Big Crunch (BB-BC) optimization method is a relatively new
technique and has already proved to be a valuable tool for structural
optimization. In the present study the exceptional efficiency of the
method is demonstrated by an example of the lay-up optimization
of multilayered anisotropic cylinders based on a three-dimensional
elasticity solution. It is shown that, due to its simplicity and speed,
the BB-BC is much more efficient for this class of problems when
compared to the genetic algorithms.
Abstract: In this paper we present a Feed-Foward Neural
Networks Autoregressive (FFNN-AR) model with genetic algorithms
training optimization in order to predict the gross domestic product
growth of six countries. Specifically we propose a kind of weighted
regression, which can be used for econometric purposes, where the
initial inputs are multiplied by the neural networks final optimum
weights from input-hidden layer of the training process. The
forecasts are compared with those of the ordinary autoregressive
model and we conclude that the proposed regression-s forecasting
results outperform significant those of autoregressive model.
Moreover this technique can be used in Autoregressive-Moving
Average models, with and without exogenous inputs, as also the
training process with genetics algorithms optimization can be
replaced by the error back-propagation algorithm.
Abstract: There are several approaches in trying to solve the
Quantitative 1Structure-Activity Relationship (QSAR) problem.
These approaches are based either on statistical methods or on
predictive data mining. Among the statistical methods, one should
consider regression analysis, pattern recognition (such as cluster
analysis, factor analysis and principal components analysis) or partial
least squares. Predictive data mining techniques use either neural
networks, or genetic programming, or neuro-fuzzy knowledge. These
approaches have a low explanatory capability or non at all. This
paper attempts to establish a new approach in solving QSAR
problems using descriptive data mining. This way, the relationship
between the chemical properties and the activity of a substance
would be comprehensibly modeled.
Abstract: There is strong evidence that water channel proteins
'aquaporins (AQPs)' are central components in plant-water relations
as well as a number of other physiological parameters. We had
previously reported the isolation of 24 plasma membrane intrinsic
protein (PIP) type AQPs. However, the gene numbers in rice and the
polyploid nature of bread wheat indicated a high probability of
further genes in the latter. The present work focused on identification
of further AQP isoforms in bread wheat. With the use of altered
primer design, we identified five genes homologous, designated
PIP1;5b, PIP2;9b, TaPIP2;2, TaPIP2;2a, TaPIP2;2b. Sequence
alignments indicate PIP1;5b, PIP2;9b are likely to be homeologues of
two previously reported genes while the other three are new genes
and could be homeologs of each other. The results indicate further
AQP diversity in wheat and the sequence data will enable physical
mapping of these genes to identify their genomes as well as genetic to
determine their association with any quantitative trait loci (QTLs)
associated with plant-water relation such as salinity or drought
tolerance.
Abstract: Nowadays, driving support systems, such as car
navigation systems, are getting common, and they support drivers in
several aspects. It is important for driving support systems to detect
status of driver's consciousness. Particularly, detecting driver's
drowsiness could prevent drivers from collisions caused by drowsy
driving. In this paper, we discuss the various artificial detection
methods for detecting driver's drowsiness processing technique. This
system is based on facial images analysis for warning the driver of
drowsiness or in attention to prevent traffic accidents.
Abstract: A direct connection between ElectroEncephaloGram
(EEG) and the genetic information of individuals has been
investigated by neurophysiologists and psychiatrists since 1960-s;
and it opens a new research area in the science. This paper focuses on
the person identification based on feature extracted from the EEG
which can show a direct connection between EEG and the genetic
information of subjects. In this work the full EO EEG signal of
healthy individuals are estimated by an autoregressive (AR) model
and the AR parameters are extracted as features. Here for feature
vector constitution, two methods have been proposed; in the first
method the extracted parameters of each channel are used as a
feature vector in the classification step which employs a competitive
neural network and in the second method a combination of different
channel parameters are used as a feature vector. Correct classification
scores at the range of 80% to 100% reveal the potential of our
approach for person classification/identification and are in agreement
to the previous researches showing evidence that the EEG signal
carries genetic information. The novelty of this work is in the
combination of AR parameters and the network type (competitive
network) that we have used. A comparison between the first and the
second approach imply preference of the second one.
Abstract: Sudoku is a kind of logic puzzles. Each puzzle consists
of a board, which is a 9×9 cells, divided into nine 3×3 subblocks
and a set of numbers from 1 to 9. The aim of this puzzle is to
fill in every cell of the board with a number from 1 to 9 such
that in every row, every column, and every subblock contains each
number exactly one. Sudoku puzzles belong to combinatorial problem
(NP complete). Sudoku puzzles can be solved by using a variety of
techniques/algorithms such as genetic algorithms, heuristics, integer
programming, and so on. In this paper, we propose a new approach for
solving Sudoku which is by modelling them as block-world problems.
In block-world problems, there are a number of boxes on the table
with a particular order or arrangement. The objective of this problem
is to change this arrangement into the targeted arrangement with the
help of two types of robots. In this paper, we present three models
for Sudoku. We modellized Sudoku as parameterized multi-agent
systems. A parameterized multi-agent system is a multi-agent system
which consists of several uniform/similar agents and the number of
the agents in the system is stated as the parameter of this system. We
use Temporal Logic of Actions (TLA) for formalizing our models.
Abstract: The treatment of the industrial wastewater can be
particularly difficult in the presence of toxic compounds. Excessive
concentration of Chromium in soluble form is toxic to a wide variety
of living organisms. Biological removal of heavy metals using natural
and genetically engineered microorganisms has aroused great interest
because of its lower impact on the environment. Ralston
metallidurans, formerly known as Alcaligenes eutrophus is a LProteobacterium
colonizing industrial wastewater with a high content
of heavy metals. Tris-buffered mineral salt medium was used for
growing Alcaligenes eutrophus AE104 (pEBZ141). The cells were
cultivated for 18 h at 30 oC in Tris-buffered mineral salt medium
containing 3 mM disodium sulphate and 46 mM sodium gluconate as
the carbon source. The cells were harvested by centrifugation,
washed, and suspended in 10 mM Tris HCl, pH 7.0, containing 46
mM sodium gluconate, and 5 mM Chromium. Interaction among
induction of chr resistance determinant, and chromate reduction have
been demonstrated. Results of this study show that the above bacteria
can be very useful for bioremediation of chromium from industrial
wastewater.
Abstract: In this paper performance of Puma 560
manipulator is being compared for hybrid gradient descent
and least square method learning based ANFIS controller with
hybrid Genetic Algorithm and Generalized Pattern Search
tuned radial basis function based Neuro-Fuzzy controller.
ANFIS which is based on Takagi Sugeno type Fuzzy
controller needs prior knowledge of rule base while in radial
basis function based Neuro-Fuzzy rule base knowledge is not
required. Hybrid Genetic Algorithm with generalized Pattern
Search is used for tuning weights of radial basis function
based Neuro- fuzzy controller. All the controllers are checked
for butterfly trajectory tracking and results in the form of
Cartesian and joint space errors are being compared. ANFIS
based controller is showing better performance compared to
Radial Basis Function based Neuro-Fuzzy Controller but rule
base independency of RBF based Neuro-Fuzzy gives it an
edge over ANFIS
Abstract: K-Means (KM) is considered one of the major
algorithms widely used in clustering. However, it still has some
problems, and one of them is in its initialization step where it is
normally done randomly. Another problem for KM is that it
converges to local minima. Genetic algorithms are one of the
evolutionary algorithms inspired from nature and utilized in the field
of clustering. In this paper, we propose two algorithms to solve the
initialization problem, Genetic Algorithm Initializes KM (GAIK) and
KM Initializes Genetic Algorithm (KIGA). To show the effectiveness
and efficiency of our algorithms, a comparative study was done
among GAIK, KIGA, Genetic-based Clustering Algorithm (GCA),
and FCM [19].
Abstract: Edge detection is usually the first step in medical
image processing. However, the difficulty increases when a
conventional kernel-based edge detector is applied to ultrasonic
images with a textural pattern and speckle noise. We designed an
adaptive diffusion filter to remove speckle noise while preserving the
initial edges detected by using a Sobel edge detector. We also propose
a genetic algorithm for edge selection to form complete boundaries of
the detected entities. We designed two fitness functions to evaluate
whether a criterion with a complex edge configuration can render a
better result than a simple criterion such as the strength of gradient.
The edges obtained by using a complex fitness function are thicker and
more fragmented than those obtained by using a simple fitness
function, suggesting that a complex edge selecting scheme is not
necessary for good edge detection in medical ultrasonic images;
instead, a proper noise-smoothing filter is the key.
Abstract: Sickle cell anemia is a recessive genetic disease
caused by the presence in the red blood cell, of abnormal hemoglobin
called hemoglobin S. It results from the replacement in the beta chain
of the acid glutamic acid by valin at position 6. Topics may be
homozygous (SS) or heterozygous (AS) most often
asymptomatic. Other mutations result in compound heterozygous:
- Synthesis of hemoglobin C mutation in the sixth leucin codon
(heterozygous SC);
- ß-thalassemia (heterozygous S-ß thalassemia).
SS homozygous, heterozygous SC and S- ß -thalassemia are grouped
under the major sickle cell syndromes.
To make a laboratory diagnosis of hemoglobinopathies in a
portion of the population in region of Batna, our study was
conducted on 115 patients with suspected sickle cell anemia, all cases
have benefited from hematological tests as blood count (count RBC,
calculated erythrocyte indices, MCV and MCHC, measuring the
hemoglobin concentration) and a biochemical test in this case
electrophoresis CAPILLARYS HEMOGLOBIN (E).
The results showed:
27 cases of sickle cell anemia were found on 115 suspected cases,
73,03% homozygous sickle cell disease and 59,25% sickle cell trait.
Finally, the double heterozygous S/C, represent the incidence rate of
3, 70%.
Abstract: An evolutionary computing technique for solving initial value problems in Ordinary Differential Equations is proposed in this paper. Neural network is used as a universal approximator while the adaptive parameters of neural networks are optimized by genetic algorithm. The solution is achieved on the continuous grid of time instead of discrete as in other numerical techniques. The comparison is carried out with classical numerical techniques and the solution is found with a uniform accuracy of MSE ≈ 10-9 .
Abstract: This paper shows possibility of extraction Social,
Group and Individual Mind from Multiple Agents Rule Bases. Types
those Rule bases are selected as two fuzzy systems, namely
Mambdani and Takagi-Sugeno fuzzy system. Their rule bases are
describing (modeling) agent behavior. Modifying of agent behavior
in the time varying environment will be provided by learning fuzzyneural
networks and optimization of their parameters with using
genetic algorithms in development system FUZNET. Finally,
extraction Social, Group and Individual Mind from Multiple Agents
Rule Bases are provided by Cognitive analysis and Matching
criterion.
Abstract: The present work deals with optimization of cascade refrigeration system using eco friendly refrigerants pair R507A and R23. R507A is azeotropic mixture composed of HFC refrigerants R125/R143a (50%/50% by wt.). R23 is a single component HFC refrigerant used as replacement to CFC refrigerant R13 in low temperature applications. These refrigerants have zero ozone depletion potential and are non-flammable. Optimization of R507AR23 cascade refrigeration system performance parameters such as minimum work required, refrigeration effect, coefficient of performance and exergetic efficiency was carried out in terms of eight operating parameters- combinations using Genetic Algorithm tool. The eight operating parameters include (1) low side evaporator temperature (2) high side condenser temperature (3) temperature difference in the cascade heat exchanger (4) low side condenser temperature (5) low side degree of subcooling (6) high side degree of subcooling (7) low side degree of superheating (8) high side degree of superheating. Results show that for minimum work system should operate at high temperature in low side evaporator, low temperature in high side condenser, low temperature difference in cascade condenser, high temperature in low side condenser and low degree of subcooling and superheating in both side. For maximum refrigeration effect system should operate at high temperature in low side evaporator, high temperature in high side condenser, high temperature difference in cascade condenser, low temperature in low side condenser and higher degree of subcooling in LT and HT side. For maximum coefficient of performance and exergetic efficiency, system should operate at high temperature in low side evaporator, low temperature in high side condenser, low temperature difference in cascade condenser, high temperature in low side condenser and higher degree of subcooling and superheating in low side of the system.
Abstract: This paper presents an overview of the multiobjective shortest path problem (MSPP) and a review of essential and recent issues regarding the methods to its solution. The paper further explores a multiobjective evolutionary algorithm as applied to the MSPP and describes its behavior in terms of diversity of solutions, computational complexity, and optimality of solutions. Results show that the evolutionary algorithm can find diverse solutions to the MSPP in polynomial time (based on several network instances) and can be an alternative when other methods are trapped by the tractability problem.
Abstract: In order to study seed yield and seed yield
components in bean under reduced irrigation condition and
assessment drought tolerance of genotypes, 15 lines of White beans
were evaluated in two separate RCB design with 3 replications under
stress and non stress conditions. Analysis of variance showed that
there were significant differences among varieties in terms of traits
under study, indicating the existence of genetic variation among
varieties. The results indicate that drought stress reduced seed yield,
number of seed per plant, biological yield and number of pod in
White been. In non stress condition, yield was highly correlated with
the biological yield, whereas in stress condition it was highly
correlated with harvest index. Results of stepwise regression showed
that, selection can we done based on, biological yield, harvest index,
number of seed per pod, seed length, 100 seed weight. Result of path
analysis showed that the highest direct effect, being positive, was
related to biological yield in non stress and to harvest index in stress
conditions. Factor analysis were accomplished in stress and nonstress
condition a, there were 4 factors that explained more than 76
percent of total variations. We used several selection indices such as
Stress Susceptibility Index ( SSI ), Geometric Mean Productivity (
GMP ), Mean Productivity ( MP ), Stress Tolerance Index ( STI ) and
Tolerance Index ( TOL ) to study drought tolerance of genotypes, we
found that the best Stress Index for selection tolerance genotypes
were STI, GMP and MP were the greatest correlations between these
Indices and seed yield under stress and non stress conditions. In
classification of genotypes base on phenotypic characteristics, using
cluster analysis ( UPGMA ), all allels classified in 5 separate groups
in stress and non stress conditions.
Abstract: The particular interests of this paper is to explore if the simple Genetic Algorithms (GA) starts with population of only two individuals and applying different crossover technique over these parents to produced 104 children, each one has different attributes inherited from their parents; is better than starting with population of 100 individuals; and using only one type crossover (order crossover OX). For this reason we implement GA with 52 different crossover techniques; each one produce two children; which means 104 different children will be produced and this may discover more search space, also we implement classic GA with order crossover and many experiments were done over 3 Travel Salesman Problem (TSP) to find out which method is better, and according to the results we can say that GA with Multi-crossovers is much better.
Abstract: We present a hybrid architecture of recurrent neural
networks (RNNs) inspired by hidden Markov models (HMMs). We
train the hybrid architecture using genetic algorithms to learn and
represent dynamical systems. We train the hybrid architecture on a
set of deterministic finite-state automata strings and observe the
generalization performance of the hybrid architecture when presented
with a new set of strings which were not present in the training data
set. In this way, we show that the hybrid system of HMM and RNN
can learn and represent deterministic finite-state automata. We ran
experiments with different sets of population sizes in the genetic
algorithm; we also ran experiments to find out which weight
initializations were best for training the hybrid architecture. The
results show that the hybrid architecture of recurrent neural networks
inspired by hidden Markov models can train and represent dynamical
systems. The best training and generalization performance is
achieved when the hybrid architecture is initialized with random real
weight values of range -15 to 15.
Abstract: Neem is a highly heterozygous and commercially
important perennial plant. Conventionally, it is propagated by seeds
which loose viability within two weeks. Strictly cross pollinating
nature of the plant causes serious barrier to the genetic improvement
by conventional methods. Alternative methods of tree improvement
such as somatic hybridization, mutagenesis and genetic
transformation require an efficient in vitro plant regeneration system.
In this regard, somatic embryogenesis particularly secondary somatic
embryogenesis may offer an effective system for large scale plant
propagation without affecting the clonal fidelity of the regenerants. It
can be used for synthetic seed production, which further bolsters
conservation of this tree species which is otherwise very difficult
The present report describes the culture conditions necessary to
induce and maintain repetitive somatic embryogenesis, for the first
time, in neem. Out of various treatments tested, the somatic embryos
were induced directly from immature zygotic embryos of neem on
MS + TDZ (0.1 μM) + ABA (4 μM), in more than 76 % cultures.
Direct secondary somatic embryogenesis occurred from primary
somatic embryos on MS + IAA (5 μM) + GA3 (5 μM) in 12.5 %
cultures. Embryogenic competence of the explant as well as of the
primary embryos was maintained for a long period by repeated
subcultures at frequent intervals. A maximum of 10 % of these
somatic embryos were converted into plantlets.