Abstract: Genome profiling (GP), a genotype based technology, which exploits random PCR and temperature gradient gel electrophoresis, has been successful in identification/classification of organisms. In this technology, spiddos (Species identification dots) and PaSS (Pattern similarity score) were employed for measuring the closeness (or distance) between genomes. Based on the closeness (PaSS), we can buildup phylogenetic trees of the organisms. We noticed that the topology of the tree is rather robust against the experimental fluctuation conveyed by spiddos. This fact was confirmed quantitatively in this study by computer-simulation, providing the limit of the reliability of this highly powerful methodology. As a result, we could demonstrate the effectiveness of the GP approach for identification/classification of organisms.
Abstract: By systematically applying different engineering
methods, difficult financial problems become approachable. Using a
combination of theory and techniques such as wavelet transform,
time series data mining, Markov chain based discrete stochastic
optimization, and evolutionary algorithms, this work formulated a
strategy to characterize and forecast non-linear time series. It
attempted to extract typical features from the volatility data sets of
S&P100 and S&P500 indices that include abrupt drops, jumps and
other non-linearity. As a result, accuracy of forecasting has reached
an average of over 75% surpassing any other publicly available
results on the forecast of any financial index.
Abstract: Atherosclerosis is the condition in which an artery
wall thickens as the result of a build-up of fatty materials such as
cholesterol. It is a syndrome affecting arterial blood vessels, a
chronic inflammatory response in the walls of arteries, in large part
due to the accumulation of macrophage white blood cells and
promoted by low density (especially small particle) lipoproteins
(plasma proteins that carry cholesterol and triglycerides) without
adequate removal of fats and cholesterol from the macrophages by
functional high density lipoproteins (HDL). It is commonly referred
to as a hardening or furring of the arteries. It is caused by the
formation of multiple plaques within the arteries.
Abstract: Unmanned aerial vehicles (UAVs) performing their
operations for a long time have been attracting much attention in
military and civil aviation industries for the past decade. The
applicable field of UAV is changing from the military purpose only to
the civil one. Because of their low operation cost, high reliability and
the necessity of various application areas, numerous development
programs have been initiated around the world. To obtain the optimal
solutions of the design variable (i.e., sectional airfoil profile, wing
taper ratio and sweep) for high performance of UAVs, both the lift and
lift-to-drag ratio are maximized whereas the pitching moment should
be minimized, simultaneously. It is found that the lift force and
lift-to-drag ratio are linearly dependent and a unique and dominant
solution are existed. However, a trade-off phenomenon is observed
between the lift-to-drag ratio and pitching moment. As the result of
optimization, sixty-five (65) non-dominated Pareto individuals at the
cutting edge of design spaces that are decided by airfoil shapes can be
obtained.
Abstract: Bio-electrical responses obtained from freshwater
sediments by employing microbial fuel cell (MFC) technology were
investigated in this experimental study. During the electricity
generation, organic matter in the sediment was microbially oxidized
under anaerobic conditions with an electrode serving as a terminal
electron acceptor. It was found that the sediment organic matter
(SOM) associated with electrochemically-active electrodes became
more humified, aromatic, and polydispersed, and had a higher average
molecular weight, together with the decrease in the quantity of SOM.
The alteration of characteristics of the SOM was analogous to that
commonly observed in the early stage of SOM diagenetic process (i.e.,
humification). These findings including an elevation of the sediment
redox potential present a possibility of the MFC technology as a new
soil/sediment remediation technique based on its potential benefits:
non-destructive electricity generation and bioremediation.
Abstract: Several researchers have proposed methods about
combination of Genetic Algorithm (GA) and Fuzzy Logic (the use of
GA to obtain fuzzy rules and application of fuzzy logic in
optimization of GA). In this paper, we suggest a new method in
which fuzzy decision making is used to improve the performance of
genetic algorithm. In the suggested method, we determine the alleles
that enhance the fitness of chromosomes and try to insert them to the
next generation.
In this algorithm we try to present an innovative vaccination in the
process of reproduction in genetic algorithm, with considering the
trade off between exploration and exploitation.
Abstract: In this paper we consider a nonlinear feedback
control called augmented automatic choosing control (AACC)
using the automatic choosing functions of gradient optimization
type for nonlinear systems. Constant terms which arise from sectionwise
linearization of a given nonlinear system are treated as
coefficients of a stable zero dynamics. Parameters included in the
control are suboptimally selected by minimizing the Hamiltonian
with the aid of the genetic algorithm. This approach is applied to
a field excitation control problem of power system to demonstrate
the splendidness of the AACC. Simulation results show that the
new controller can improve performance remarkably well.
Abstract: An adaptive fuzzy PID controller with gain scheduling is proposed in this paper. The structure of the proposed gain scheduled fuzzy PID (GS_FPID) controller consists of both fuzzy PI-like controller and fuzzy PD-like controller. Both of fuzzy PI-like and PD-like controllers are weighted through adaptive gain scheduling, which are also determined by fuzzy logic inference. A modified genetic algorithm called accumulated genetic algorithm is designed to learn the parameters of fuzzy inference system. In order to learn the number of fuzzy rules required for the TSK model, the fuzzy rules are learned in an accumulated way. In other words, the parameters learned in the previous rules are accumulated and updated along with the parameters in the current rule. It will be shown that the proposed GS_FPID controllers learned by the accumulated GA perform well for not only the regular linear systems but also the higher order and time-delayed systems.
Abstract: The problem of lot sizing, sequencing and scheduling
multiple products in flow line production systems has been studied
by several authors. Almost all of the researches in this area assumed
that setup times and costs are sequence –independent even though
sequence dependent setups are common in practice. In this paper we
present a new mixed integer non linear program (MINLP) and a
heuristic method to solve the problem in sequence dependent case.
Furthermore, a genetic algorithm has been developed which applies
this constructive heuristic to generate initial population. These two
proposed solution methods are compared on randomly generated
problems. Computational results show a clear superiority of our
proposed GA for majority of the test problems.
Abstract: Fault-proneness of a software module is the
probability that the module contains faults. A correlation exists
between the fault-proneness of the software and the measurable
attributes of the code (i.e. the static metrics) and of the testing (i.e.
the dynamic metrics). Early detection of fault-prone software
components enables verification experts to concentrate their time and
resources on the problem areas of the software system under
development. This paper introduces Genetic Algorithm based
software fault prediction models with Object-Oriented metrics. The
contribution of this paper is that it has used Metric values of JEdit
open source software for generation of the rules for the classification
of software modules in the categories of Faulty and non faulty
modules and thereafter empirically validation is performed. The
results shows that Genetic algorithm approach can be used for
finding the fault proneness in object oriented software components.
Abstract: A novel method of learning complex fuzzy decision regions in the n-dimensional feature space is proposed. Through the fuzzy decision regions, a given pattern's class membership value of every class is determined instead of the conventional crisp class the pattern belongs to. The n-dimensional fuzzy decision region is approximated by union of hyperellipsoids. By explicitly parameterizing these hyperellipsoids, the decision regions are determined by estimating the parameters of each hyperellipsoid.Genetic Algorithm is applied to estimate the parameters of each region component. With the global optimization ability of GA, the learned decision region can be arbitrarily complex.
Abstract: The effect of different combinations of response
feedback on the performance of active control system on nonlinear
frames has been studied in this paper. To this end different feedback
combinations including displacement, velocity, acceleration and full
response feedback have been utilized in controlling the response of
an eight story bilinear hysteretic frame which has been subjected to a
white noise excitation and controlled by eight actuators which could
fully control the frame. For active control of nonlinear frame
Newmark nonlinear instantaneous optimal control algorithm has been
used which a diagonal matrix has been selected for weighting
matrices in performance index. For optimal design of active control
system while the objective has been to reduce the maximum drift to
below the yielding level, Distributed Genetic Algorithm (DGA) has
been used to determine the proper set of weighting matrices. The
criteria to assess the effect of each combination of response feedback
have been the minimum required control force to reduce the
maximum drift to below the yielding drift. The results of numerical
simulation show that the performance of active control system is
dependent on the type of response feedback where the velocity
feedback is more effective in designing optimal control system in
comparison with displacement and acceleration feedback. Also using
full feedback of response in controller design leads to minimum
control force amongst other combinations. Also the distributed
genetic algorithm shows acceptable convergence speed in solving the
optimization problem of designing active control systems.
Abstract: Scale defects are common surface defects in hot steel rolling. The modelling of such defects is problematic and their causes are not straightforward. In this study, we investigated genetic algorithms in search for a mathematical solution to scale formation. For this research, a high-dimensional data set from hot steel rolling process was gathered. The synchronisation of the variables as well as the allocation of the measurements made on the steel strip were solved before the modelling phase.
Abstract: A feature weighting and selection method is proposed
which uses the structure of a weightless neuron and exploits the
principles that govern the operation of Genetic Algorithms and
Evolution. Features are coded onto chromosomes in a novel way
which allows weighting information regarding the features to be
directly inferred from the gene values. The proposed method is
significant in that it addresses several problems concerned with
algorithms for feature selection and weighting as well as providing
significant advantages such as speed, simplicity and suitability for
real-time systems.
Abstract: In the present work, the performance of the particle
swarm optimization and the genetic algorithm compared as a typical
geometry design problem. The design maximizes the heat transfer
rate from a given fin volume. The analysis presumes that a linear
temperature distribution along the fin. The fin profile generated using
the B-spline curves and controlled by the change of control point
coordinates. An inverse method applied to find the appropriate fin
geometry yield the linear temperature distribution along the fin
corresponds to optimum design. The numbers of the populations, the
count of iterations and time to convergence measure efficiency.
Results show that the particle swarm optimization is most efficient
for geometry optimization.
Abstract: This paper proposes a bi-objective model for the
facility location problem under a congestion system. The idea of the
model is motivated by applications of locating servers in bank
automated teller machines (ATMS), communication networks, and so
on. This model can be specifically considered for situations in which
fixed service facilities are congested by stochastic demand within
queueing framework. We formulate this model with two perspectives
simultaneously: (i) customers and (ii) service provider. The
objectives of the model are to minimize (i) the total expected
travelling and waiting time and (ii) the average facility idle-time.
This model represents a mixed-integer nonlinear programming
problem which belongs to the class of NP-hard problems. In addition,
to solve the model, two metaheuristic algorithms including nondominated
sorting genetic algorithms (NSGA-II) and non-dominated
ranking genetic algorithms (NRGA) are proposed. Besides, to
evaluate the performance of the two algorithms some numerical
examples are produced and analyzed with some metrics to determine
which algorithm works better.
Abstract: In this paper, we propose a new hybrid learning model for stock market indices prediction by adding a passive congregation term to the standard hybrid model comprising Particle Swarm Optimization (PSO) with Genetic Algorithm (GA) operators in training Neural Networks (NN). This new passive congregation term is based on the cooperation between different particles in determining new positions rather than depending on the particles selfish thinking without considering other particles positions, thus it enables PSO to perform both the local and global search instead of only doing the local search. Experiment study carried out on the most famous European stock market indices in both long term and short term prediction shows significantly the influence of the passive congregation term in improving the prediction accuracy compared to standard hybrid model.
Abstract: The conventional GA combined with a local search
algorithm, such as the 2-OPT, forms a hybrid genetic algorithm(HGA)
for the traveling salesman problem (TSP). However, the geometric
properties which are problem specific knowledge can be used to
improve the search process of the HGA. Some tour segments (edges)
of TSPs are fine while some maybe too long to appear in a short tour.
This knowledge could constrain GAs to work out with fine tour
segments without considering long tour segments as often.
Consequently, a new algorithm is proposed, called intelligent-OPT
hybrid genetic algorithm (IOHGA), to improve the GA and the 2-OPT
algorithm in order to reduce the search time for the optimal solution.
Based on the geometric properties, all the tour segments are assigned
2-level priorities to distinguish between good and bad genes. A
simulation study was conducted to evaluate the performance of the
IOHGA. The experimental results indicate that in general the IOHGA
could obtain near-optimal solutions with less time and better accuracy
than the hybrid genetic algorithm with simulated annealing algorithm
(HGA(SA)).
Abstract: In the recent past, there has been an increasing interest
in applying evolutionary methods to Knowledge Discovery in
Databases (KDD) and a number of successful applications of Genetic
Algorithms (GA) and Genetic Programming (GP) to KDD have been
demonstrated. The most predominant representation of the
discovered knowledge is the standard Production Rules (PRs) in the
form If P Then D. The PRs, however, are unable to handle
exceptions and do not exhibit variable precision. The Censored
Production Rules (CPRs), an extension of PRs, were proposed by
Michalski & Winston that exhibit variable precision and supports an
efficient mechanism for handling exceptions. A CPR is an
augmented production rule of the form:
If P Then D Unless C, where C (Censor) is an exception to the rule.
Such rules are employed in situations, in which the conditional
statement 'If P Then D' holds frequently and the assertion C holds
rarely. By using a rule of this type we are free to ignore the exception
conditions, when the resources needed to establish its presence are
tight or there is simply no information available as to whether it
holds or not. Thus, the 'If P Then D' part of the CPR expresses
important information, while the Unless C part acts only as a switch
and changes the polarity of D to ~D.
This paper presents a classification algorithm based on evolutionary
approach that discovers comprehensible rules with exceptions in the
form of CPRs.
The proposed approach has flexible chromosome encoding, where
each chromosome corresponds to a CPR. Appropriate genetic
operators are suggested and a fitness function is proposed that
incorporates the basic constraints on CPRs. Experimental results are
presented to demonstrate the performance of the proposed algorithm.
Abstract: This paper and its companion (Part 2) deal with
modeling and optimization of two NP-hard problems in production
planning of flexible manufacturing system (FMS), part type selection
problem and loading problem. The part type selection problem and
the loading problem are strongly related and heavily influence the
system-s efficiency and productivity. The complexity of the problems
is harder when flexibilities of operations such as the possibility of
operation processed on alternative machines with alternative tools are
considered. These problems have been modeled and solved
simultaneously by using real coded genetic algorithms (RCGA)
which uses an array of real numbers as chromosome representation.
These real numbers can be converted into part type sequence and
machines that are used to process the part types. This first part of the
papers focuses on the modeling of the problems and discussing how
the novel chromosome representation can be applied to solve the
problems. The second part will discuss the effectiveness of the
RCGA to solve various test bed problems.