Abstract: Project managers are the ultimate responsible for the
overall characteristics of a project, i.e. they should deliver the project
on time with minimum cost and with maximum quality. It is vital for
any manager to decide a trade-off between these conflicting
objectives and they will be benefited of any scientific decision
support tool. Our work will try to determine optimal solutions (rather
than a single optimal solution) from which the project manager will
select his desirable choice to run the project. In this paper, the
problem in project scheduling notated as
(1,T|cpm,disc,mu|curve:quality,time,cost) will be studied. The
problem is multi-objective and the purpose is finding the Pareto
optimal front of time, cost and quality of a project
(curve:quality,time,cost), whose activities belong to a start to finish
activity relationship network (cpm) and they can be done in different
possible modes (mu) which are non-continuous or discrete (disc), and
each mode has a different cost, time and quality . The project is
constrained to a non-renewable resource i.e. money (1,T). Because
the problem is NP-Hard, to solve the problem, a meta-heuristic is
developed based on a version of genetic algorithm specially adapted
to solve multi-objective problems namely FastPGA. A sample project
with 30 activities is generated and then solved by the proposed
method.
Abstract: In recent years, there has been an increasing interest
toward the use of bovine genotyped embryos for commercial embryo
transfer programs. Biopsy of a few cells in morulla stage is essential
for preimplantation genetic diagnosis (PGD). Low amount of DNA
have limited performing the several molecular analyses within PGD
analyses. Whole genome amplification (WGA) promises to eliminate
this problem. We evaluated the possibility and performance of an
improved primer extension preamplification (I-PEP) method with a
range of starting bovine genomic DNA from 1-8 cells into the WGA
reaction. We optimized a short and simple I-PEP (ssI-PEP) procedure
(~3h). This optimized WGA method was assessed by 6 loci specific
polymerase chain reactions (PCRs), included restriction fragments
length polymorphism (RFLP). Optimized WGA procedure possesses
enough sensitivity for molecular genetic analyses through the few
input cells. This is a new era for generating characterized bovine
embryos in preimplantation stage.
Abstract: Y chromosome microdeletions are the most common
genetic cause of male infertility and screening for these
microdeletions in azoospermic or severely oligospermic men is now
standard practice. Analysis of the Y chromosome in men with
azoospermia or severe oligozoospermia has resulted in the
identification of three regions in the euchromatic part of the long arm
of the human Y chromosome (Yq11) that are frequently deleted in
men with otherwise unexplained spermatogenic failure. PCR analysis
of microdeletions in the AZFa, AZFb and AZFc regions of the
human Y chromosome is an important screening tool. The aim of this
study was to analyse the type of microdeletions in men with fertility
disorders in Slovakia. We evaluated 227 patients with azoospermia
and with normal karyotype. All patient samples were analyzed
cytogenetically. For PCR amplification of sequence-tagged sites
(STS) of the AZFa, AZFb and AZFc regions of the Y chromosome
was used Devyser AZF set. Fluorescently labeled primers for all
markers in one multiplex PCR reaction were used and for automated
visualization and identification of the STS markers we used genetic
analyzer ABi 3500xl (Life Technologies). We reported 13 cases of
deletions in the AZF region 5,73%. Particular types of deletions were
recorded in each region AZFa,b,c .The presence of microdeletions in
the AZFc region was the most frequent. The study confirmed that
percentage of microdeletions in the AZF region is low in Slovak
azoospermic patients, but important from a prognostic view.
Abstract: In this paper a hybrid technique of Genetic Algorithm
and Simulated Annealing (HGASA) is applied for Fractal Image
Compression (FIC). With the help of this hybrid evolutionary
algorithm effort is made to reduce the search complexity of matching
between range block and domain block. The concept of Simulated
Annealing (SA) is incorporated into Genetic Algorithm (GA) in order
to avoid pre-mature convergence of the strings. One of the image
compression techniques in the spatial domain is Fractal Image
Compression but the main drawback of FIC is that it involves more
computational time due to global search. In order to improve the
computational time along with acceptable quality of the decoded
image, HGASA technique has been proposed. Experimental results
show that the proposed HGASA is a better method than GA in terms
of PSNR for Fractal image Compression.
Abstract: Flow-shop scheduling problem (FSP) deals with the
scheduling of a set of jobs that visit a set of machines in the same
order. The FSP is NP-hard, which means that an efficient algorithm
for solving the problem to optimality is unavailable. To meet the
requirements on time and to minimize the make-span performance of
large permutation flow-shop scheduling problems in which there are
sequence dependent setup times on each machine, this paper
develops one hybrid genetic algorithms (HGA). Proposed HGA
apply a modified approach to generate population of initial
chromosomes and also use an improved heuristic called the iterated
swap procedure to improve initial solutions. Also the author uses
three genetic operators to make good new offspring. The results are
compared to some recently developed heuristics and computational
experimental results show that the proposed HGA performs very
competitively with respect to accuracy and efficiency of solution.
Abstract: This study assessed the effects of climate change on
Thai soybeans under simulation situations. Our study is focused on
temperature variability and effects on growth, yield, and genetic
changes in 2 generations of Chiang Mai 60 cultivars. In the
experiment, soybeans were exposed to 3 levels of air temperature for
8 h day-1 in an open top chamber for 2 cropping periods. Air
temperature levels in each treatment were controlled at 30-33°C (±
2.3) for LT-treatment, 33-36°C ( ± 2.4) for AT-treatment, and 36-40
°C ( ± 3.2) for HT-treatment, respectively. Positive effects of high
temperature became obvious at the maturing stage when yield
significantly increased in both cropping periods. Results in growth
indicated that shoot length at the pre-maturing stage
(V3-R3) was more positively affected by high temperature than at the
maturing stage. However, the positive effect on growth under high
temperature was not found in the 2nd cropping period. Finally, genetic
changes were examined in phenotype characteristics by the AFLPs
technique. The results showed that the high temperature factor clearly
caused genetic change in the soybeans and showed more alteration in
the 2nd cropping period.
Abstract: Interactions among proteins are the basis of various
life events. So, it is important to recognize and research protein
interaction sites. A control set that contains 149 protein molecules
were used here. Then 10 features were extracted and 4 sample sets
that contained 9 sliding windows were made according to features.
These 4 sample sets were calculated by Radial Basis Functional neutral
networks which were optimized by Particle Swarm Optimization
respectively. Then 4 groups of results were obtained. Finally, these 4
groups of results were integrated by decision fusion (DF) and Genetic
Algorithm based Selected Ensemble (GASEN). A better accuracy was
got by DF and GASEN. So, the integrated methods were proved to
be effective.
Abstract: The Artificial immune systems algorithms are Meta
heuristic optimization method, which are used for clustering and
pattern recognition applications are abundantly. These algorithms in
multimodal optimization problems are more efficient than genetic
algorithms. A major drawback in these algorithms is their slow
convergence to global optimum and their weak stability can be
considered in various running of these algorithms. In this paper,
improved Artificial Immune System Algorithm is introduced for the
first time to overcome its problems of artificial immune system. That
use of the small size of a local search around the memory antibodies
is used for improving the algorithm efficiently. The credibility of the
proposed approach is evaluated by simulations, and it is shown that
the proposed approach achieves better results can be achieved
compared to the standard artificial immune system algorithms
Abstract: The aim of the current study is to develop a numerical
tool that is capable of achieving an optimum shape and design of
hyperbolic cooling towers based on coupling a non-linear finite
element model developed in-house and a genetic algorithm
optimization technique. The objective function is set to be the
minimum weight of the tower. The geometric modeling of the tower
is represented by means of B-spline curves. The finite element
method is applied to model the elastic buckling behaviour of a tower
subjected to wind pressure and dead load. The study is divided into
two main parts. The first part investigates the optimum shape of the
tower corresponding to minimum weight assuming constant
thickness. The study is extended in the second part by introducing the
shell thickness as one of the design variables in order to achieve an
optimum shape and design. Design, functionality and practicality
constraints are applied.
Abstract: The well known NP-complete problem of the Traveling Salesman Problem (TSP) is coded in genetic form. A software system is proposed to determine the optimum route for a Traveling Salesman Problem using Genetic Algorithm technique. The system starts from a matrix of the calculated Euclidean distances between the cities to be visited by the traveling salesman and a randomly chosen city order as the initial population. Then new generations are then created repeatedly until the proper path is reached upon reaching a stopping criterion. This search is guided by a solution evaluation function.
Abstract: In this study, control performance of a smart base
isolation system consisting of a friction pendulum system (FPS) and a
magnetorheological (MR) damper has been investigated. A fuzzy
logic controller (FLC) is used to modulate the MR damper so as to
minimize structural acceleration while maintaining acceptable base
displacement levels. To this end, a multi-objective optimization
scheme is used to optimize parameters of membership functions and
find appropriate fuzzy rules. To demonstrate effectiveness of the
proposed multi-objective genetic algorithm for FLC, a numerical
study of a smart base isolation system is conducted using several
historical earthquakes. It is shown that the proposed method can find
optimal fuzzy rules and that the optimized FLC outperforms not only a
passive control strategy but also a human-designed FLC and a
conventional semi-active control algorithm.
Abstract: Many studies have focused on the nonlinear analysis
of electroencephalography (EEG) mainly for the characterization of
epileptic brain states. It is assumed that at least two states of the
epileptic brain are possible: the interictal state characterized by a
normal apparently random, steady-state EEG ongoing activity; and
the ictal state that is characterized by paroxysmal occurrence of
synchronous oscillations and is generally called in neurology, a
seizure.
The spatial and temporal dynamics of the epileptogenic process is
still not clear completely especially the most challenging aspects of
epileptology which is the anticipation of the seizure. Despite all the
efforts we still don-t know how and when and why the seizure
occurs. However actual studies bring strong evidence that the
interictal-ictal state transition is not an abrupt phenomena. Findings
also indicate that it is possible to detect a preseizure phase.
Our approach is to use the neural network tool to detect interictal
states and to predict from those states the upcoming seizure ( ictal
state). Analysis of the EEG signal based on neural networks is used
for the classification of EEG as either seizure or non-seizure. By
applying prediction methods it will be possible to predict the
upcoming seizure from non-seizure EEG.
We will study the patients admitted to the epilepsy monitoring
unit for the purpose of recording their seizures. Preictal, ictal, and
post ictal EEG recordings are available on such patients for analysis
The system will be induced by taking a body of samples then
validate it using another. Distinct from the two first ones a third body
of samples is taken to test the network for the achievement of
optimum prediction. Several methods will be tried 'Backpropagation
ANN' and 'RBF'.
Abstract: The identification and elimination of bad
measurements is one of the basic functions of a robust state estimator
as bad data have the effect of corrupting the results of state
estimation according to the popular weighted least squares method.
However this is a difficult problem to handle especially when dealing
with multiple errors from the interactive conforming type. In this
paper, a self adaptive genetic based algorithm is proposed. The
algorithm utilizes the results of the classical linearized normal
residuals approach to tune the genetic operators thus instead of
making a randomized search throughout the whole search space it is
more likely to be a directed search thus the optimum solution is
obtained at very early stages(maximum of 5 generations). The
algorithm utilizes the accumulating databases of already computed
cases to reduce the computational burden to minimum. Tests are
conducted with reference to the standard IEEE test systems. Test
results are very promising.
Abstract: The shortest path routing problem is a multiobjective
nonlinear optimization problem with constraints. This problem has
been addressed by considering Quality of service parameters, delay
and cost objectives separately or as a weighted sum of both
objectives. Multiobjective evolutionary algorithms can find multiple
pareto-optimal solutions in one single run and this ability makes them
attractive for solving problems with multiple and conflicting
objectives. This paper uses an elitist multiobjective evolutionary
algorithm based on the Non-dominated Sorting Genetic Algorithm
(NSGA), for solving the dynamic shortest path routing problem in
computer networks. A priority-based encoding scheme is proposed
for population initialization. Elitism ensures that the best solution
does not deteriorate in the next generations. Results for a sample test
network have been presented to demonstrate the capabilities of the
proposed approach to generate well-distributed pareto-optimal
solutions of dynamic routing problem in one single run. The results
obtained by NSGA are compared with single objective weighting
factor method for which Genetic Algorithm (GA) was applied.
Abstract: The design of a pattern classifier includes an attempt
to select, among a set of possible features, a minimum subset of
weakly correlated features that better discriminate the pattern classes.
This is usually a difficult task in practice, normally requiring the
application of heuristic knowledge about the specific problem
domain. The selection and quality of the features representing each
pattern have a considerable bearing on the success of subsequent
pattern classification. Feature extraction is the process of deriving
new features from the original features in order to reduce the cost of
feature measurement, increase classifier efficiency, and allow higher
classification accuracy. Many current feature extraction techniques
involve linear transformations of the original pattern vectors to new
vectors of lower dimensionality. While this is useful for data
visualization and increasing classification efficiency, it does not
necessarily reduce the number of features that must be measured
since each new feature may be a linear combination of all of the
features in the original pattern vector. In this paper a new approach is
presented to feature extraction in which feature selection, feature
extraction, and classifier training are performed simultaneously using
a genetic algorithm. In this approach each feature value is first
normalized by a linear equation, then scaled by the associated weight
prior to training, testing, and classification. A knn classifier is used to
evaluate each set of feature weights. The genetic algorithm optimizes
a vector of feature weights, which are used to scale the individual
features in the original pattern vectors in either a linear or a nonlinear
fashion. By this approach, the number of features used in classifying
can be finely reduced.
Abstract: Economic dispatch (ED) is considered to be one of the
key functions in electric power system operation. This paper presents
a new hybrid approach based genetic algorithm (GA) to economic
dispatch problems. GA is most commonly used optimizing algorithm
predicated on principal of natural evolution. Utilization of chaotic
queue with GA generates several neighborhoods of near optimal
solutions to keep solution variation. It could avoid the search process
from becoming pre-mature. For the objective of chaotic queue
generation, utilization of tent equation as opposed to logistic equation
results in improvement of iterative speed. The results of the proposed
approach were compared in terms of fuel cost, with existing
differential evolution and other methods in literature.
Abstract: Before performing polymerase chain reactions (PCR), a feasible primer set is required. Many primer design methods have been proposed for design a feasible primer set. However, the majority of these methods require a relatively long time to obtain an optimal solution since large quantities of template DNA need to be analyzed. Furthermore, the designed primer sets usually do not provide a specific PCR product. In recent years, evolutionary computation has been applied to PCR primer design and yielded promising results. In this paper, a particle swarm optimization (PSO) algorithm is proposed to solve primer design problems associated with providing a specific product for PCR experiments. A test set of the gene CYP1A1, associated with a heightened lung cancer risk was analyzed and the comparison of accuracy and running time with the genetic algorithm (GA) and memetic algorithm (MA) was performed. A comparison of results indicated that the proposed PSO method for primer design finds optimal or near-optimal primer sets and effective PCR products in a relatively short time.
Abstract: Genetic Algorithms (GAs) are direct searching
methods which require little information from design space. This
characteristic beside robustness of these algorithms makes them to be
very popular in recent decades. On the other hand, while this method
is employed, there is no guarantee to achieve optimum results. This
obliged designer to run such algorithms more than one time to
achieve more reliable results. There are many attempts to modify the
algorithms to make them more efficient. In this paper, by application
of fractal dimension (particularly, Box Counting Method), the
complexity of design space are established for determination of
mutation and crossover probabilities (Pm and Pc). This methodology
is followed by a numerical example for more clarification. It is
concluded that this modification will improve efficiency of GAs and
make them to bring about more reliable results especially for design
space with higher fractal dimensions.
Abstract: Genetic algorithms (GAs) have been widely used for
global optimization problems. The GA performance depends highly
on the choice of the search space for each parameter to be optimized.
Often, this choice is a problem-based experience. The search space
being a set of potential solutions may contain the global optimum
and/or other local optimums. A bad choice of this search space
results in poor solutions. In this paper, our approach consists in
extending the search space boundaries during the GA optimization,
only when it is required. This leads to more diversification of GA
population by new solutions that were not available with fixed search
space boundaries. So, these dynamic search spaces can improve the
GA optimization performances. The proposed approach is applied to
power system stabilizer optimization for multimachine power system
(16-generator and 68-bus). The obtained results are evaluated and
compared with those obtained by ordinary GAs. Eigenvalue analysis
and nonlinear system simulation results show the effectiveness of the
proposed approach to damp out the electromechanical oscillation and
enhance the global system stability.
Abstract: Evolvable hardware (EHW) refers to a selfreconfiguration
hardware design, where the configuration is under
the control of an evolutionary algorithm (EA). A lot of research has
been done in this area several different EA have been introduced.
Every time a specific EA is chosen for solving a particular problem,
all its components, such as population size, initialization, selection
mechanism, mutation rate, and genetic operators, should be selected
in order to achieve the best results. In the last three decade a lot of
research has been carried out in order to identify the best parameters
for the EA-s components for different “test-problems". However
different researchers propose different solutions. In this paper the
behaviour of mutation rate on (1+λ) evolution strategy (ES) for
designing logic circuits, which has not been done before, has been
deeply analyzed. The mutation rate for an EHW system modifies
values of the logic cell inputs, the cell type (for example from AND
to NOR) and the circuit output. The behaviour of the mutation has
been analyzed based on the number of generations, genotype
redundancy and number of logic gates used for the evolved circuits.
The experimental results found provide the behaviour of the mutation
rate to be used during evolution for the design and optimization of
logic circuits. The researches on the best mutation rate during the last
40 years are also summarized.