Abstract: We intend to point out the differences which exist
between the classical Gini concentration coefficient and a proposed
bipolarization index defined for an arbitrary random variable which
have a finite support.
In fact Gini's index measures only the "poverty degree" for the
individuals from a given population taking into consideration their
wages. The Gini coefficient is not so sensitive to the significant
income variations in the "rich people class" .
In practice there are multiple interdependent relations between the
pauperization and the socio-economical polarization phenomena. The
presence of a strong pauperization aspect inside the population
induces often a polarization effect in this society. But the
pauperization and the polarization phenomena are not identical. For
this reason it isn't always adequate to use a Gini type coefficient,
based on the Lorenz order, to estimate the bipolarization level of the
individuals from the studied population.
The present paper emphasizes these ideas by considering two
families of random variables which have a linear or a triangular type
distributions. In addition, the continuous variation, depending on the
parameter "time" of the chosen distributions, could simulate a real
dynamical evolution of the population.
Abstract: To create a solution for a specific problem in machine
learning, the solution is constructed from the data or by use a search
method. Genetic algorithms are a model of machine learning that can
be used to find nearest optimal solution. While the great advantage of
genetic algorithms is the fact that they find a solution through
evolution, this is also the biggest disadvantage. Evolution is inductive,
in nature life does not evolve towards a good solution but it evolves
away from bad circumstances. This can cause a species to evolve into
an evolutionary dead end. In order to reduce the effect of this
disadvantage we propose a new a learning tool (criteria) which can be
included into the genetic algorithms generations to compare the
previous population and the current population and then decide
whether is effective to continue with the previous population or the
current population, the proposed learning tool is called as Keeping
Efficient Population (KEP). We applied a GA based on KEP to the
production line layout problem, as a result KEP keep the evaluation
direction increases and stops any deviation in the evaluation.
Abstract: The advent of modern technology shadows its impetus repercussions on successful Legacy systems making them obsolete with time. These systems have evolved the large organizations in major problems in terms of new business requirements, response time, financial depreciation and maintenance. Major difficulty is due to constant system evolution and incomplete, inconsistent and obsolete documents which a legacy system tends to have. The myriad dimensions of these systems can only be explored by incorporating reverse engineering, in this context, is the best method to extract useful artifacts and by exploring these artifacts for reengineering existing legacy systems to meet new requirements of organizations. A case study is conducted on six different type of software systems having source code in different programming languages using the architectural recovery framework.
Abstract: Clustering techniques have received attention in many areas including engineering, medicine, biology and data mining. The purpose of clustering is to group together data points, which are close to one another. The K-means algorithm is one of the most widely used techniques for clustering. However, K-means has two shortcomings: dependency on the initial state and convergence to local optima and global solutions of large problems cannot found with reasonable amount of computation effort. In order to overcome local optima problem lots of studies done in clustering. This paper is presented an efficient hybrid evolutionary optimization algorithm based on combining Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), called PSO-ACO, for optimally clustering N object into K clusters. The new PSO-ACO algorithm is tested on several data sets, and its performance is compared with those of ACO, PSO and K-means clustering. The simulation results show that the proposed evolutionary optimization algorithm is robust and suitable for handing data clustering.
Abstract: Computer-mediated communication technologies which provide for virtual communities have typically evolved in a cross-dichotomous manner, such that technical constructs of the technology have evolved independently from the social environment of the community. The present paper analyses some limitations of current implementations of computer-mediated communication technology that are implied by such a dichotomy, and discusses their inhibiting effects on possible developments of virtual communities. A Socio-Technical Indicator Model is introduced that utilizes integrated feedback to describe, simulate and operationalise increasing representativeness within a variety of structurally and parametrically diverse systems. In illustration, applications of the model are briefly described for financial markets and for eco-systems. A detailed application is then provided to resolve the aforementioned technical limitations of moderation on the evolution of virtual communities. The application parameterises virtual communities to function as self-transforming social-technical systems which are sensitive to emergent and shifting community values as products of on-going communications within the collective.
Abstract: A dynamic software risk assessment model is
presented. Analogies between dynamic financial analysis and
software risk assessment models are established and based on these
analogies it suggested that dynamic risk model for software projects
is the way to move forward for the risk assessment of software
project. It is shown how software risk assessment change during
different phases of a software project and hence requires a dynamic
risk assessment model to capture these variations. Further evolution
of dynamic financial analysis models is discussed and mapped to the
evolution of software risk assessment models.
Abstract: Application of Information Technology (IT) has
revolutionized the functioning of business all over the world. Its
impact has been felt mostly among the information of dependent
industries. Tourism is one of such industry. The conceptual
framework in this study represents an innovation of travel
information searching system on mobile devices which is used as
tools to deliver travel information (such as hotels, restaurants, tourist
attractions and souvenir shops) for each user by travelers
segmentation based on data mining technique to segment the tourists-
behavior patterns then match them with tourism products and
services. This system innovation is designed to be a knowledge
incremental learning. It is a marketing strategy to support business to
respond traveler-s demand effectively.
Abstract: Evolvable hardware (EHW) is a developing field that
applies evolutionary algorithm (EA) to automatically design circuits,
antennas, robot controllers etc. A lot of research has been done in this
area and several different EAs have been introduced to tackle
numerous problems, as scalability, evolvability etc. However every
time a specific EA is chosen for solving a particular task, 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 the
selection of the right parameters for the EA-s components for solving
different “test-problems" has been investigated. In this paper the
behaviour of mutation rate for designing logic circuits, which has not
been done before, has been deeply analyzed. The mutation rate for an
EHW system modifies the number of inputs of each logic gates, the
functionality (for example from AND to NOR) and the connectivity
between logic gates. The behaviour of the mutation has been
analyzed based on the number of generations, genotype redundancy
and number of logic gates for the evolved circuits. The experimental
results found provide the behaviour of the mutation rate during
evolution for the design and optimization of simple logic circuits.
The experimental results propose the best mutation rate to be used for
designing combinational logic circuits. The research presented is
particular important for those who would like to implement a
dynamic mutation rate inside the evolutionary algorithm for evolving
digital circuits. The researches on the mutation rate during the last 40
years are also summarized.
Abstract: Since 1992, year where Hugo de Garis has published
the first paper on Evolvable Hardware (EHW), a period of intense
creativity has followed. It has been actively researched, developed
and applied to various problems. Different approaches have been
proposed that created three main classifications: extrinsic, mixtrinsic
and intrinsic EHW. Each of these solutions has a real interest.
Nevertheless, although the extrinsic evolution generates some
excellent results, the intrinsic systems are not so advanced. This
paper suggests 3 possible solutions to implement the run-time
configuration intrinsic EHW system: FPGA-based Run-Time
Configuration system, JBits-based Run-Time Configuration system
and Multi-board functional-level Run-Time Configuration system.
The main characteristic of the proposed architectures is that they are
implemented on Field Programmable Gate Array. A comparison of
proposed solutions demonstrates that multi-board functional-level
run-time configuration is superior in terms of scalability, flexibility
and the implementation easiness.
Abstract: The evolutionary design of electronic circuits, or
evolvable hardware, is a discipline that allows the user to
automatically obtain the desired circuit design. The circuit
configuration is under the control of evolutionary algorithms. Several
researchers have used evolvable hardware to design electrical
circuits. Every time that one particular algorithm is selected to carry
out the evolution, it is necessary that all its parameters, such as
mutation rate, population size, selection mechanisms etc. are tuned in
order to achieve the best results during the evolution process. This
paper investigates the abilities of evolution strategy to evolve digital
logic circuits based on programmable logic array structures when
different mutation rates are used. Several mutation rates (fixed and
variable) are analyzed and compared with each other to outline the
most appropriate choice to be used during the evolution of
combinational logic circuits. The experimental results outlined in this
paper are important as they could be used by every researcher who
might need to use the evolutionary algorithm to design digital logic
circuits.
Abstract: Architecture education was based on apprenticeship
models and its nature has not changed much during long period but
the Source of changes was its evaluation process and system. It is
undeniable that art and architecture education is completely based on
transmitting knowledge from instructor to students. In contrast to
other majors this transmitting is by iteration and practice and studio
masters try to control the design process and improving skills in the
form of supervision and criticizing. Also the evaluation will end by
giving marks to students- achievements. Therefore the importance of
the evaluation and assessment role is obvious and it is not irrelevant
to say that if we want to know about the architecture education
system, we must first study its assessment procedures. The evolution
of these changes in western countries has literate and documented
well. However it seems that this procedure has unregarded in
Malaysia and there is a severe lack of research and documentation in
this area. Malaysia as an under developing and multicultural country
which is involved different races and cultures is a proper origin for
scrutinizing and understanding the evaluation systems and
acceptability amount of current implemented models to keep the
evaluation and assessment procedure abreast with needs of different
generations, cultures and even genders. This paper attempts to
answer the questions of how evaluation and assessments are
performed and how students perceive this evaluation system in the
context Malaysia. The main advantage of this work is that it
contributes in international debate on evaluation model.
Abstract: This paper proposes an application of the differential
evolution (DE) algorithm for solving the economic dispatch problem
(ED). Furthermore, the regenerating population procedure added to
the conventional DE in order to improve escaping the local minimum
solution. To test performance of DE algorithm, three thermal
generating units with valve-point loading effects is used for testing.
Moreover, investigating the DE parameters is presented. The
simulation results show that the DE algorithm, which had been
adjusted parameters, is better convergent time than other optimization
methods.
Abstract: Hair is a non homogenous complex material which
can be associated with a polymer. It is made up 95% of Keratin.
Hair has a great social significance for human beings. In the High
Middle Ages, for example, long hairs have been reserved for kings
and nobles.
Most common interest in hair is focused on hair growth, hair types
and hair care, but hair is also an important biomaterial which can
vary depending on ethnic origin or on age, hair colour for example
can be a sign of ethnic ancestry or age (dark hair for Asiatic, blond
hair for Caucasian and white hair for old people in general).
In this context, different approaches have been conducted to
determine the differences in mechanical properties and characterize
the fracture topography at the surface of hair depending on its type
and its age.
A tensile testing machine was especially designed to achieve
tensile tests on hair. This device is composed of a microdisplacement
system and a force sensor whose peak load is limited to
3N. The curves and the values extracted from each experiment, allow
us to compare the evolution of the mechanical properties from one
hair to another.
Observations with a Scanning Electron Microscope (SEM) and
with an interferometer were made on different hairs. Thus, it is
possible to access the cuticle state and the fracture topography for
each category.
Abstract: In this contribution an innovative platform is being
presented that integrates intelligent agents and evolutionary
computation techniques in legacy e-learning environments. It
introduces the design and development of a scalable and
interoperable integration platform supporting:
I) various assessment agents for e-learning environments,
II) a specific resource retrieval agent for the provision of
additional information from Internet sources matching the
needs and profile of the specific user and
III) a genetic algorithm designed to extract efficient information
(classifying rules) based on the students- answering input
data.
The agents are implemented in order to provide intelligent
assessment services based on computational intelligence techniques
such as Bayesian Networks and Genetic Algorithms.
The proposed Genetic Algorithm (GA) is used in order to extract
efficient information (classifying rules) based on the students-
answering input data. The idea of using a GA in order to fulfil this
difficult task came from the fact that GAs have been widely used in
applications including classification of unknown data.
The utilization of new and emerging technologies like web
services allows integrating the provided services to any web based
legacy e-learning environment.
Abstract: An evolutionary method whose selection and recombination
operations are based on generalization error-bounds of
support vector machine (SVM) can select a subset of potentially
informative genes for SVM classifier very efficiently [7]. In this
paper, we will use the derivative of error-bound (first-order criteria)
to select and recombine gene features in the evolutionary process,
and compare the performance of the derivative of error-bound with
the error-bound itself (zero-order) in the evolutionary process. We
also investigate several error-bounds and their derivatives to compare
the performance, and find the best criteria for gene selection
and classification. We use 7 cancer-related human gene expression
datasets to evaluate the performance of the zero-order and first-order
criteria of error-bounds. Though both criteria have the same strategy
in theoretically, experimental results demonstrate the best criterion
for microarray gene expression data.
Abstract: Many real-world optimization problems involve multiple conflicting objectives and the use of evolutionary algorithms to solve the problems has attracted much attention recently. This paper investigates the application of multi-objective optimization technique for the design of a Thyristor Controlled Series Compensator (TCSC)-based controller to enhance the performance of a power system. The design objective is to improve both rotor angle stability and system voltage profile. A Genetic Algorithm (GA) based solution technique is applied to generate a Pareto set of global optimal solutions to the given multi-objective optimisation problem. Further, a fuzzy-based membership value assignment method is employed to choose the best compromise solution from the obtained Pareto solution set. Simulation results are presented to show the effectiveness and robustness of the proposed approach.
Abstract: The goal of Gene Expression Analysis is to understand the processes that underlie the regulatory networks and pathways controlling inter-cellular and intra-cellular activities. In recent times microarray datasets are extensively used for this purpose. The scope of such analysis has broadened in recent times towards reconstruction of gene networks and other holistic approaches of Systems Biology. Evolutionary methods are proving to be successful in such problems and a number of such methods have been proposed. However all these methods are based on processing of genotypic information. Towards this end, there is a need to develop evolutionary methods that address phenotypic interactions together with genotypic interactions. We present a novel evolutionary approach, called Phenomic algorithm, wherein the focus is on phenotypic interaction. We use the expression profiles of genes to model the interactions between them at the phenotypic level. We apply this algorithm to the yeast sporulation dataset and show that the algorithm can identify gene networks with relative ease.
Abstract: Breast skin-line estimation and breast segmentation is an important pre-process in mammogram image processing and computer-aided diagnosis of breast cancer. Limiting the area to be processed into a specific target region in an image would increase the accuracy and efficiency of processing algorithms. In this paper we are presenting a new algorithm for estimating skin-line and breast segmentation using fast marching algorithm. Fast marching is a partial-differential equation based numerical technique to track evolution of interfaces. We have introduced some modifications to the traditional fast marching method, specifically to improve the accuracy of skin-line estimation and breast tissue segmentation. Proposed modifications ensure that the evolving front stops near the desired boundary. We have evaluated the performance of the algorithm by using 100 mammogram images taken from mini-MIAS database. The results obtained from the experimental evaluation indicate that this algorithm explains 98.6% of the ground truth breast region and accuracy of the segmentation is 99.1%. Also this algorithm is capable of partially-extracting nipple when it is available in the profile.
Abstract: Due to the three- dimensional flow pattern interacting with bed material, the process of local scour around bridge piers is complex. Modeling 3D flow field and scour hole evolution around a bridge pier is more feasible nowadays because the computational cost and computational time have significantly decreased. In order to evaluate local flow and scouring around a bridge pier, a completely three-dimensional numerical model, SSIIM program, was used. The model solves 3-D Navier-Stokes equations and a bed load conservation equation. The model was applied to simulate local flow and scouring around a bridge pier in a large natural river with four piers. Computation for 1 day of flood condition was carried out to predict the maximum local scour depth. The results show that the SSIIM program can be used efficiently for simulating the scouring in natural rivers. The results also showed that among the various turbulence models, the k-ω model gives more reasonable results.
Abstract: This paper presents the solution of power economic dispatch (PED) problem of generating units with valve point effects and multiple fuel options using Self-Adaptive Differential Evolution (SDE) algorithm. The global optimal solution by mathematical approaches becomes difficult for the realistic PED problem in power systems. The Differential Evolution (DE) algorithm is found to be a powerful evolutionary algorithm for global optimization in many real problems. In this paper the key parameters of control in DE algorithm such as the crossover constant CR and weight applied to random differential F are self-adapted. The PED problem formulation takes into consideration of nonsmooth fuel cost function due to valve point effects and multi fuel options of generator. The proposed approach has been examined and tested with the numerical results of PED problems with thirteen-generation units including valve-point effects, ten-generation units with multiple fuel options neglecting valve-point effects and ten-generation units including valve-point effects and multiple fuel options. The test results are promising and show the effectiveness of proposed approach for solving PED problems.