Abstract: Although Face detection is not a recent activity in the
field of image processing, it is still an open area for research. The
greatest step in this field is the work reported by Viola and its recent
analogous is Huang et al. Both of them use similar features and also
similar training process. The former is just for detecting upright
faces, but the latter can detect multi-view faces in still grayscale
images using new features called 'sparse feature'. Finding these
features is very time consuming and inefficient by proposed methods.
Here, we propose a new approach for finding sparse features using a
genetic algorithm system. This method requires less computational
cost and gets more effective features in learning process for face
detection that causes more accuracy.
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: An enhanced particle swarm optimization algorithm
(PSO) is presented in this work to solve the non-convex OPF
problem that has both discrete and continuous optimization variables.
The objective functions considered are the conventional quadratic
function and the augmented quadratic function. The latter model
presents non-differentiable and non-convex regions that challenge
most gradient-based optimization algorithms. The optimization
variables to be optimized are the generator real power outputs and
voltage magnitudes, discrete transformer tap settings, and discrete
reactive power injections due to capacitor banks. The set of equality
constraints taken into account are the power flow equations while the
inequality ones are the limits of the real and reactive power of the
generators, voltage magnitude at each bus, transformer tap settings,
and capacitor banks reactive power injections. The proposed
algorithm combines PSO with Newton-Raphson algorithm to
minimize the fuel cost function. The IEEE 30-bus system with six
generating units is used to test the proposed algorithm. Several cases
were investigated to test and validate the consistency of detecting
optimal or near optimal solution for each objective. Results are
compared to solutions obtained using sequential quadratic
programming and Genetic Algorithms.
Abstract: The evolution of technology and construction techniques has enabled the upgrading of transport networks. In particular, the high-speed rail networks allow convoys to peak at above 300 km/h. These structures, however, often significantly impact the surrounding environment. Among the effects of greater importance are the ones provoked by the soundwave connected to train transit. The wave propagation affects the quality of life in areas surrounding the tracks, often for several hundred metres. There are substantial damages to properties (buildings and land), in terms of market depreciation. The present study, integrating expertise in acoustics, computering and evaluation fields, outlines a useful model to select project paths so as to minimize the noise impact and reduce the causes of possible litigation. It also facilitates the rational selection of initiatives to contain the environmental damage to the already existing railway tracks. The research is developed with reference to the Italian regulatory framework (usually more stringent than European and international standards) and refers to a case study concerning the high speed network in Italy.
Abstract: Text categorization is the problem of classifying text documents into a set of predefined classes. After a preprocessing step the documents are typically represented as large sparse vectors. When training classifiers on large collections of documents, both the time and memory restrictions can be quite prohibitive. This justifies the application of features selection methods to reduce the dimensionality of the document-representation vector. Four feature selection methods are evaluated: Random Selection, Information Gain (IG), Support Vector Machine (called SVM_FS) and Genetic Algorithm with SVM (GA_FS). We showed that the best results were obtained with SVM_FS and GA_FS methods for a relatively small dimension of the features vector comparative with the IG method that involves longer vectors, for quite similar classification accuracies. Also we present a novel method to better correlate SVM kernel-s parameters (Polynomial or Gaussian kernel).
Abstract: In this paper a systematic method via H∞ control
design is proposed to select a sensor set that satisfies a number
of input criteria for a MAGLEV suspension system. The proposed
method recovers a number of optimised controllers for each possible
sensor set that satisfies the performance and constraint criteria using
evolutionary algorithms.
Abstract: In this paper the design of maximally flat linear phase
finite impulse response (FIR) filters is considered. The problem is
handled with totally two different approaches. The first one is
completely deterministic numerical approach where the problem is
formulated as a Linear Complementarity Problem (LCP). The other
one is based on a combination of Markov Random Fields (MRF's)
approach with messy genetic algorithm (MGA). Markov Random
Fields (MRFs) are a class of probabilistic models that have been
applied for many years to the analysis of visual patterns or textures.
Our objective is to establish MRFs as an interesting approach to
modeling messy genetic algorithms. We establish a theoretical result
that every genetic algorithm problem can be characterized in terms of
a MRF model. This allows us to construct an explicit probabilistic
model of the MGA fitness function and introduce the Ising MGA.
Experimentations done with Ising MGA are less costly than those
done with standard MGA since much less computations are involved.
The least computations of all is for the LCP. Results of the LCP,
random search, random seeded search, MGA, and Ising MGA are
discussed.
Abstract: Detection of incipient abnormal events is important to
improve safety and reliability of machine operations and reduce losses
caused by failures. Improper set-ups or aligning of parts often leads to
severe problems in many machines. The construction of prediction
models for predicting faulty conditions is quite essential in making
decisions on when to perform machine maintenance. This paper
presents a multivariate calibration monitoring approach based on the
statistical analysis of machine measurement data. The calibration
model is used to predict two faulty conditions from historical reference
data. This approach utilizes genetic algorithms (GA) based variable
selection, and we evaluate the predictive performance of several
prediction methods using real data. The results shows that the
calibration model based on supervised probabilistic principal
component analysis (SPPCA) yielded best performance in this work.
By adopting a proper variable selection scheme in calibration models,
the prediction performance can be improved by excluding
non-informative variables from their model building steps.
Abstract: This paper presents performance comparison of three estimation techniques used for peak load forecasting in power systems. The three optimum estimation techniques are, genetic algorithms (GA), least error squares (LS) and, least absolute value filtering (LAVF). The problem is formulated as an estimation problem. Different forecasting models are considered. Actual recorded data is used to perform the study. The performance of the above three optimal estimation techniques is examined. Advantages of each algorithms are reported and discussed.
Abstract: This paper analyses the performance of a genetic algorithm using a new concept, namely a fractional-order dynamic fitness function, for the synthesis of combinational logic circuits. The experiments reveal superior results in terms of speed and convergence to achieve a solution.
Abstract: In this paper, we present optimal control for
movement and trajectory planning for four degrees-of-freedom robot
using Fuzzy Logic (FL) and Genetic Algorithms (GAs). We have
evaluated using Fuzzy Logic (FL) and Genetic Algorithms (GAs)
for four degree-of-freedom (4 DOF) robotics arm, Uncertainties like;
Movement, Friction and Settling Time in robotic arm movement
have been compensated using Fuzzy logic and Genetic Algorithms.
The development of a fuzzy genetic optimization algorithm is
presented and discussed. The result are compared only GA and
Fuzzy GA. This paper describes genetic algorithms, which is
designed to optimize robot movement and trajectory. Though the
model represents is a general model for redundant structures and
could represent any n-link structures. The result is a complete
trajectory planning with Fuzzy logic and Genetic algorithms
demonstrating the flexibility of this technique of artificial
intelligence.
Abstract: System is using multiple processors for computing and information processing, is increasing rapidly speed operation of these systems compared with single processor systems, very significant impact on system performance is increased .important differences to yield a single multi-processor cpu, the scheduling policies, to reduce the implementation time of all processes. Notwithstanding the famous algorithms such as SPT, LPT, LSPT and RLPT for scheduling and there, but none led to the answer are not optimal.In this paper scheduling using genetic algorithms and innovative way to finish the whole process faster that we do and the result compared with three algorithms we mentioned.
Abstract: Selective harmonic elimination-pulse width modulation techniques offer a tight control of the harmonic spectrum of a given voltage waveform generated by a power electronic converter along with a low number of switching transitions. Traditional optimization methods suffer from various drawbacks, such as prolonged and tedious computational steps and convergence to local optima; thus, the more the number of harmonics to be eliminated, the larger the computational complexity and time. This paper presents a novel method for output voltage harmonic elimination and voltage control of PWM AC/AC voltage converters using the principle of hybrid Real-Coded Genetic Algorithm-Pattern Search (RGA-PS) method. RGA is the primary optimizer exploiting its global search capabilities, PS is then employed to fine tune the best solution provided by RGA in each evolution. The proposed method enables linear control of the fundamental component of the output voltage and complete elimination of its harmonic contents up to a specified order. Theoretical studies have been carried out to show the effectiveness and robustness of the proposed method of selective harmonic elimination. Theoretical results are validated through simulation studies using PSIM software package.
Abstract: In the era of great competition, understanding and satisfying
customers- requirements are the critical tasks for a company
to make a profits. Customer relationship management (CRM) thus
becomes an important business issue at present. With the help of
the data mining techniques, the manager can explore and analyze
from a large quantity of data to discover meaningful patterns and
rules. Among all methods, well-known association rule is most
commonly seen. This paper is based on Apriori algorithm and uses
genetic algorithms combining a data mining method to discover fuzzy
classification rules. The mined results can be applied in CRM to
help decision marker make correct business decisions for marketing
strategies.
Abstract: Recently, Genetic Algorithms (GA) and Differential
Evolution (DE) algorithm technique have attracted considerable
attention among various modern heuristic optimization techniques.
Since the two approaches are supposed to find a solution to a given
objective function but employ different strategies and computational
effort, it is appropriate to compare their performance. This paper
presents the application and performance comparison of DE and GA
optimization techniques, for flexible ac transmission system
(FACTS)-based controller design. The design objective is to enhance
the power system stability. The design problem of the FACTS-based
controller is formulated as an optimization problem and both the PSO
and GA optimization techniques are employed to search for optimal
controller parameters. The performance of both optimization
techniques has been compared. Further, the optimized controllers are
tested on a weekly connected power system subjected to different
disturbances, and their performance is compared with the
conventional power system stabilizer (CPSS). The eigenvalue
analysis and non-linear simulation results are presented and
compared to show the effectiveness of both the techniques in
designing a FACTS-based controller, to enhance power system
stability.
Abstract: An economic operation scheduling problem of a
hydro-thermal power generation system has been properly solved by
the proposed multipath adaptive tabu search algorithm (MATS). Four
reservoirs with their own hydro plants and another one thermal plant
are integrated to be a studied system used to formulate the objective
function under complicated constraints, eg water managements,
power balance and thermal generator limits. MATS with four subsearch
units (ATSs) and two stages of discarding mechanism (DM),
has been setting and trying to solve the problem through 25 trials
under function evaluation criterion. It is shown that MATS can
provide superior results with respect to single ATS and other
previous methods, genetic algorithms (GA) and differential evolution
(DE).
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: Feature and model selection are in the center of
attention of many researches because of their impact on classifiers-
performance. Both selections are usually performed separately but
recent developments suggest using a combined GA-SVM approach to
perform them simultaneously. This approach improves the
performance of the classifier identifying the best subset of variables
and the optimal parameters- values. Although GA-SVM is an
effective method it is computationally expensive, thus a rough
method can be considered. The paper investigates a joined approach
of Genetic Algorithm and kernel matrix criteria to perform
simultaneously feature and model selection for SVM classification
problem. The purpose of this research is to improve the classification
performance of SVM through an efficient approach, the Kernel
Matrix Genetic Algorithm method (KMGA).
Abstract: In the control theory one attempts to find a controller
that provides the best possible performance with respect to some
given measures of performance. There are many sorts of controllers
e.g. a typical PID controller, LQR controller, Fuzzy controller etc. In
the paper will be introduced polynomial controller with novel tuning
method which is based on the special pole placement encoding
scheme and optimization by Genetic Algorithms (GA). The examples
will show the performance of the novel designed polynomial
controller with comparison to common PID controller.
Abstract: Several optimization algorithms specifically applied to
the problem of Operation Planning of Hydrothermal Power Systems
have been developed and are used. Although providing solutions to
various problems encountered, these algorithms have some
weaknesses, difficulties in convergence, simplification of the original
formulation of the problem, or owing to the complexity of the
objective function. Thus, this paper presents the development of a
computational tool for solving optimization problem identified and to
provide the User an easy handling. Adopted as intelligent
optimization technique, Genetic Algorithms and programming
language Java. First made the modeling of the chromosomes, then
implemented the function assessment of the problem and the
operators involved, and finally the drafting of the graphical interfaces
for access to the User. The program has managed to relate a coherent
performance in problem resolution without the need for
simplification of the calculations together with the ease of
manipulating the parameters of simulation and visualization of output
results.