Abstract: Today, the treatment of cancer, in a relatively short
period, with minimum adverse effects is a great concern for
oncologists. In this paper, based on a recently used mathematical
model for cancer, a guideline has been proposed for the amount
and duration of drug doses for cancer treatment by immunotherapy.
Dynamically speaking, the mathematical ordinary differential
equation (ODE) model of cancer has different equilibrium points;
one of them is unstable, which is called the no tumor equilibrium
point. In this paper, based on the number of tumor cells an
intelligent soft computing controller (a combination of fuzzy logic
controller and genetic algorithm), decides regarding the amount
and duration of drug doses, to eliminate the tumor cells and
stabilize the unstable point in a relatively short time. Two different
immunotherapy approaches; active and adoptive, have been studied
and presented. It is shown that the rate of decay of tumor cells is
faster and the doses of drug are lower in comparison with the result
of some other literatures. It is also shown that the period of
treatment and the doses of drug in adoptive immunotherapy are
significantly less than the active method. A recommendation to
oncologists has also been presented.
Abstract: Evolutionary optimization methods such as genetic
algorithms have been used extensively for the construction site layout
problem. More recently, ant colony optimization algorithms, which
are evolutionary methods based on the foraging behavior of ants,
have been successfully applied to benchmark combinatorial
optimization problems. This paper proposes a formulation of the site
layout problem in terms of a sequencing problem that is suitable for
solution using an ant colony optimization algorithm.
In the construction industry, site layout is a very important
planning problem. The objective of site layout is to position
temporary facilities both geographically and at the correct time such
that the construction work can be performed satisfactorily with
minimal costs and improved safety and working environment. During
the last decade, evolutionary methods such as genetic algorithms
have been used extensively for the construction site layout problem.
This paper proposes an ant colony optimization model for
construction site layout. A simple case study for a highway project is
utilized to illustrate the application of the model.
Abstract: Nature is a great source of inspiration for solving
complex problems in networks. It helps to find the optimal solution.
Metaheuristic algorithm is one of the nature-inspired algorithm which
helps in solving routing problem in networks. The dynamic features,
changing of topology frequently and limited bandwidth make the
routing, challenging in MANET. Implementation of appropriate
routing algorithms leads to the efficient transmission of data in
mobile ad hoc networks. The algorithms that are inspired by the
principles of naturally-distributed/collective behavior of social
colonies have shown excellence in dealing with complex
optimization problems. Thus some of the bio-inspired metaheuristic
algorithms help to increase the efficiency of routing in ad hoc
networks. This survey work presents the overview of bio-inspired
metaheuristic algorithms which support the efficiency of routing in
mobile ad hoc networks.
Abstract: Image registration is the process of establishing point
by point correspondence between images obtained from a same
scene. This process is very useful in remote sensing, medicine,
cartography, computer vision, etc. Then, the task of registration is to
place the data into a common reference frame by estimating the
transformations between the data sets. In this work, we develop a
rigid point registration method based on the application of genetic
algorithms and Hausdorff distance. First, we extract the feature points
from both images based on the algorithm of global and local
curvature corner. After refining the feature points, we use Hausdorff
distance as similarity measure between the two data sets and for
optimizing the search space we use genetic algorithms to achieve
high computation speed for its inertial parallel. The results show the
efficiency of this method for registration of satellite images.
Abstract: Optimal supplementary damping controller design for Thyristor Controlled Series Compensator (TCSC) is presented in this paper. For the proposed controller design, a multi-objective fitness function consisting of both damping factors and real part of system electromachanical eigenvalue is used and Real- Coded Genetic Algorithm (RCGA) is employed for the optimal supplementary controller parameters. The performance of the designed supplementary TCSC-based damping controller is tested on a weakly connected power system with different disturbances and loading conditions with parameter variations. Simulation results are presented and compared with a conventional power system stabilizer and also with the TCSC-based supplementary controller when the controller parameters are not optimized to show the effectiveness and robustness of the proposed approach over a wide range of loading conditions and disturbances.
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: In order to consider the effects of the higher modes in
the pushover analysis, during the recent years several multi-modal
pushover procedures have been presented. In these methods the
response of the considered modes are combined by the square-rootof-
sum-of-squares (SRSS) rule while application of the elastic modal
combination rules in the inelastic phases is no longer valid. In this
research the feasibility of defining an efficient alternative
combination method is investigated. Two steel moment-frame
buildings denoted SAC-9 and SAC-20 under ten earthquake records
are considered. The nonlinear responses of the structures are
estimated by the directed algebraic combination of the weighted
responses of the separate modes. The weight of the each mode is
defined so that the resulted response of the combination has a
minimum error to the nonlinear time history analysis. The genetic
algorithm (GA) is used to minimize the error and optimize the weight
factors. The obtained optimal factors for each mode in different cases
are compared together to find unique appropriate weight factors for
each mode in all cases.
Abstract: This paper introduces two decoders for binary linear
codes based on Metaheuristics. The first one uses a genetic algorithm
and the second is based on a combination genetic algorithm with
a feed forward neural network. The decoder based on the genetic
algorithms (DAG) applied to BCH and convolutional codes give good
performances compared to Chase-2 and Viterbi algorithm respectively
and reach the performances of the OSD-3 for some Residue
Quadratic (RQ) codes. This algorithm is less complex for linear
block codes of large block length; furthermore their performances
can be improved by tuning the decoder-s parameters, in particular the
number of individuals by population and the number of generations.
In the second algorithm, the search space, in contrast to DAG which
was limited to the code word space, now covers the whole binary
vector space. It tries to elude a great number of coding operations
by using a neural network. This reduces greatly the complexity of
the decoder while maintaining comparable performances.
Abstract: In the context of spectrum surveillance, a new method
to recover the code of spread spectrum signal is presented, while the
receiver has no knowledge of the transmitter-s spreading sequence. In
our previous paper, we used Genetic algorithm (GA), to recover
spreading code. Although genetic algorithms (GAs) are well known
for their robustness in solving complex optimization problems, but
nonetheless, by increasing the length of the code, we will often lead
to an unacceptable slow convergence speed. To solve this problem we
introduce Particle Swarm Optimization (PSO) into code estimation in
spread spectrum communication system. In searching process for
code estimation, the PSO algorithm has the merits of rapid
convergence to the global optimum, without being trapped in local
suboptimum, and good robustness to noise. In this paper we describe
how to implement PSO as a component of a searching algorithm in
code estimation. Swarm intelligence boasts a number of advantages
due to the use of mobile agents. Some of them are: Scalability, Fault
tolerance, Adaptation, Speed, Modularity, Autonomy, and
Parallelism. These properties make swarm intelligence very attractive
for spread spectrum code estimation. They also make swarm
intelligence suitable for a variety of other kinds of channels. Our
results compare between swarm-based algorithms and Genetic
algorithms, and also show PSO algorithm performance in code
estimation process.
Abstract: In this paper a genetic algorithms approach for solving the linear and quadratic fuzzy equations Ãx̃=B̃ and Ãx̃2 + B̃x̃=C̃ , where Ã, B̃, C̃ and x̃ are fuzzy numbers is proposed by genetic algorithms. Our genetic based method initially starts with a set of random fuzzy solutions. Then in each generation of genetic algorithms, the solution candidates converge more to better fuzzy solution x̃b . In this proposed method the final reached x̃b is not only restricted to fuzzy triangular and it can be fuzzy number.
Abstract: In this contribution an innovative platform is being
presented that integrates intelligent agents in legacy e-learning environments. It introduces the design and development of a scalable
and interoperable integration platform supporting various assessment agents for e-learning environments. The agents are implemented in
order to provide intelligent assessment services to computational intelligent techniques such as Bayesian Networks and Genetic
Algorithms. The utilization of new and emerging technologies like web services allows integrating the provided services to any web
based legacy e-learning environment.
Abstract: Wireless Sensor Networks consist of small battery
powered devices with limited energy resources. once deployed, the
small sensor nodes are usually inaccessible to the user, and thus
replacement of the energy source is not feasible. Hence, One of the
most important issues that needs to be enhanced in order to improve
the life span of the network is energy efficiency. to overcome this
demerit many research have been done. The clustering is the one of
the representative approaches. in the clustering, the cluster heads
gather data from nodes and sending them to the base station. In this
paper, we introduce a dynamic clustering algorithm using genetic
algorithm. This algorithm takes different parameters into
consideration to increase the network lifetime. To prove efficiency of
proposed algorithm, we simulated the proposed algorithm compared
with LEACH algorithm using the matlab
Abstract: Today, Genetic Algorithm has been used to solve
wide range of optimization problems. Some researches conduct on
applying Genetic Algorithm to text classification, summarization
and information retrieval system in text mining process. This
researches show a better performance due to the nature of Genetic
Algorithm. In this paper a new algorithm for using Genetic
Algorithm in concept weighting and topic identification, based on
concept standard deviation will be explored.
Abstract: This paper presents a genetic algorithm based
approach for solving security constrained optimal power flow
problem (SCOPF) including FACTS devices. The optimal location of
FACTS devices are identified using an index called overload index
and the optimal values are obtained using an enhanced genetic
algorithm. The optimal allocation by the proposed method optimizes
the investment, taking into account its effects on security in terms of
the alleviation of line overloads. The proposed approach has been
tested on IEEE-30 bus system to show the effectiveness of the
proposed algorithm for solving the SCOPF problem.
Abstract: This study has investigated a vehicle Lumped
Parameter Model (LPM) in frontal crash. There are several ways for
determining spring and damper characteristics and type of problem
shall be considered as system identification. This study use Genetic
Algorithm (GA) procedure, being an effective procedure in case of
optimization issues, for optimizing errors, between target data
(experimental data) and calculated results (being obtained by
analytical solving). In this study analyzed model in 5-DOF then
compared our results with 5-DOF serial model. Finally, the response
of model due to external excitement is investigated.
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: In this paper, a TSK-type Neuro-fuzzy Inference
System that combines the features of fuzzy sets and neural networks
has been applied for the identification of MIMO systems. The procedure of adapting parameters in TSK model employs a Shuffled
Frog Leaping Algorithm (SFLA) which is inspired from the memetic evolution of a group of frogs when seeking for food. To demonstrate
the accuracy and effectiveness of the proposed controller, two nonlinear systems have been considered as the MIMO plant, and results have been compared with other learning methods based on
Particle Swarm Optimization algorithm (PSO) and Genetic
Algorithm (GA).
Abstract: Multicast Network Technology has pervaded our
lives-a few examples of the Networking Techniques and also for the
improvement of various routing devices we use. As we know the
Multicast Data is a technology offers many applications to the user
such as high speed voice, high speed data services, which is presently
dominated by the Normal networking and the cable system and
digital subscriber line (DSL) technologies. Advantages of Multi cast
Broadcast such as over other routing techniques. Usually QoS
(Quality of Service) Guarantees are required in most of Multicast
applications. The bandwidth-delay constrained optimization and we
use a multi objective model and routing approach based on genetic
algorithm that optimizes multiple QoS parameters simultaneously.
The proposed approach is non-dominated routes and the performance
with high efficiency of GA. Its betterment and high optimization has
been verified. We have also introduced and correlate the result of
multicast GA with the Broadband wireless to minimize the delay in
the path.
Abstract: The design of a gravity dam is performed through an
interactive process involving a preliminary layout of the structure
followed by a stability and stress analysis. This study presents a
method to define the optimal top width of gravity dam with genetic
algorithm. To solve the optimization task (minimize the cost of the
dam), an optimization routine based on genetic algorithms (GAs) was
implemented into an Excel spreadsheet. It was found to perform well
and GA parameters were optimized in a parametric study. Using the
parameters found in the parametric study, the top width of gravity
dam optimization was performed and compared to a gradient-based
optimization method (classic method). The accuracy of the results
was within close proximity. In optimum dam cross section, the ratio
of is dam base to dam height is almost equal to 0.85, and ratio of dam
top width to dam height is almost equal to 0.13. The computerized
methodology may provide the help for computation of the optimal
top width for a wide range of height of a gravity dam.