Abstract: Genome rearrangement is an important area in computational biology and bioinformatics. The basic problem in genome rearrangements is to compute the edit distance, i.e., the minimum number of operations needed to transform one genome into another. Unfortunately, unsigned genome rearrangement problem is NP-hard. In this study an improved ant colony optimization algorithm to approximate the edit distance is proposed. The main idea is to convert the unsigned permutation to signed permutation and evaluate the ants by using Kaplan algorithm. Two new operations are added to the standard ant colony algorithm: Replacing the worst ants by re-sampling the ants from a new probability distribution and applying the crossover operations on the best ants. The proposed algorithm is tested and compared with the improved breakpoint reversal sort algorithm by using three datasets. The results indicate that the proposed algorithm achieves better accuracy ratio than the previous methods.
Abstract: Genetic Algorithm (GA) is a powerful technique for solving optimization problems. It follows the idea of survival of the fittest - Better and better solutions evolve from previous generations until a near optimal solution is obtained. GA uses the main three operations, the selection, crossover and mutation to produce new generations from the old ones. GA has been widely used to solve optimization problems in many applications such as traveling salesman problem, airport traffic control, information retrieval (IR), reactive power optimization, job shop scheduling, and hydraulics systems such as water pipeline systems. In water pipeline systems we need to achieve some goals optimally such as minimum cost of construction, minimum length of pipes and diameters, and the place of protection devices. GA shows high performance over the other optimization techniques, moreover, it is easy to implement and use. Also, it searches a limited number of solutions.
Abstract: Previous research has indicated a variable effect of exercise on adolescents’ cognitive function. However, comparisons between studies are difficult to make due to differences in: the mode, intensity and duration of exercise employed; the components of cognitive function measured (and the tests used to assess them); and the timing of the cognitive function tests in relation to the exercise. Therefore, the aim of the present study was to assess the time course (10 and 60min post-exercise) of the effects of 15min intermittent exercise on cognitive function in adolescents. 45 adolescents were recruited to participate in the study and completed two main trials (exercise and resting) in a counterbalanced crossover design. Participants completed 15min of intermittent exercise (in cycles of 1 min exercise, 30s rest). A battery of computer based cognitive function tests (Stroop test, Sternberg paradigm and visual search test) were completed 30 min pre- and 10 and 60min post-exercise (to assess attention, working memory and perception respectively).The findings of the present study indicate that on the baseline level of the Stroop test, 10min following exercise response times were slower than at any other time point on either trial (trial by session time interaction, p = 0.0308). However, this slowing of responses also tended to produce enhanced accuracy 10min post-exercise on the baseline level of the Stroop test (trial by session time interaction, p = 0.0780). Similarly, on the complex level of the visual search test there was a slowing of response times 10 min post-exercise (trial by session time interaction, p = 0.0199). However, this was not coupled with an improvement in accuracy (trial by session time interaction, p = 0.2349). The mid-morning bout of exercise did not affect response times or accuracy across the morning on the Sternberg paradigm. In conclusion, the findings of the present study suggest an equivocal effect of exercise on adolescents' cognitive function. The mid-morning bout of exercise appears to cause a speed-accuracy trade off immediately following exercise on the Stroop test (participants become slower but more accurate), whilst slowing response times on the visual search test and having no effect on performance on the Sternberg paradigm. Furthermore, this work highlights the importance of the timing of the cognitive function tests relative to the exercise and the components of cognitive function examined in future studies.
Abstract: A numerical simulation of optimization all of electrospinning processing parameters to obtain smallest nanofiber diameter have been performed by employing genetic algorithm (GA). Fitness function in genetic algorithm methods, which was different for each parameter, was determined by simulation approach based on the Reneker’s model. Moreover, others genetic algorithm parameter, namely length of population, crossover and mutation were applied to get the optimum electrospinning processing parameters. In addition, minimum fiber diameter, 32 nm, was achieved from a simulation by applied the optimum parameters of electrospinning. This finding may be useful for process control and prediction of electrospun fiber production. In this paper, it is also compared between predicted parameters with some experimental results.
Abstract: This work focused on the interactions which occur between ester solvents and alcohol solutes. The alcohols selected ranged from the simplest alcohol (methanol) to C10-alcohols, and solubility predictions in the form of infinite dilution activity coefficients were made using the Modified UNIFAC Dortmund group contribution model. The model computation was set up on a Microsoft Excel spreadsheet specifically designed for this purpose. It was found that alcohol/ ester interactions yielded an increase in activity coefficients (i.e. became less soluble) with an increase in the size of the ester solvent molecule. Furthermore, activity coefficients decreased with an increase in the size of the alcohol solute. The activity coefficients also decreased with an increase in the degree of unsaturation of the ester hydrocarbon tail. Tertiary alcohols yielded lower activity coefficients than primary alcohols. Finally, cyclic alcohols yielded higher activity coefficients than straight-chain alcohols until a point is reached where the trend is reversed, referred to as the ‘crossover’ point.
Abstract: An efficient freeway system will be essential to the
development of Africa, and interchanges are a key to that efficiency.
Around the world, many interchanges between freeways and surface
streets, called service interchanges, are of the diamond configuration,
and interchanges using roundabouts or loop ramps are also popular.
However, many diamond interchanges have serious operational
problems, interchanges with roundabouts fail at high demand levels,
and loops use lots of expensive land. Newer service interchange
designs provide other options. The most popular new interchange
design in the US at the moment is the double crossover diamond
(DCD), also known as the diverging diamond. The DCD has
enormous potential, but also has several significant limitations.
The objectives of this paper are to review new service interchange
options and to highlight some of the main features of those
alternatives. The paper tests four conventional and seven
unconventional designs using seven measures related to efficiency,
cost, and safety.
The results show that there is no superior design in all measures
investigated. The DCD is better than most designs tested on most
measures examined. However, the DCD was only superior to all
other designs for bridge width. The DCD performed relatively poorly
for capacity and for serving pedestrians. Based on the results, African
freeway designers are encouraged to investigate the full range of
alternatives that could work at the spot of interest. Diamonds and
DCDs have their niches, but some of the other designs investigated
could be optimum at some spots.
Abstract: The Genetic Algorithm (GA) is one of the most important methods used to solve many combinatorial optimization problems. Therefore, many researchers have tried to improve the GA by using different methods and operations in order to find the optimal solution within reasonable time. This paper proposes an improved GA (IGA), where the new crossover operation, population reformulates operation, multi mutation operation, partial local optimal mutation operation, and rearrangement operation are used to solve the Traveling Salesman Problem. The proposed IGA was then compared with three GAs, which use different crossover operations and mutations. The results of this comparison show that the IGA can achieve better results for the solutions in a faster time.
Abstract: The importance of supply chain and logistics
management has been widely recognised. Effective management of
the supply chain can reduce costs and lead times and improve
responsiveness to changing customer demands. This paper proposes a
multi-matrix real-coded Generic Algorithm (MRGA) based
optimisation tool that minimises total costs associated within supply
chain logistics. According to finite capacity constraints of all parties
within the chain, Genetic Algorithm (GA) often produces infeasible
chromosomes during initialisation and evolution processes. In the
proposed algorithm, chromosome initialisation procedure, crossover
and mutation operations that always guarantee feasible solutions
were embedded. The proposed algorithm was tested using three sizes
of benchmarking dataset of logistic chain network, which are typical
of those faced by most global manufacturing companies. A half
fractional factorial design was carried out to investigate the influence
of alternative crossover and mutation operators by varying GA
parameters. The analysis of experimental results suggested that the
quality of solutions obtained is sensitive to the ways in which the
genetic parameters and operators are set.
Abstract: This paper presents an efficient approach to feeder
reconfiguration for power loss reduction and voltage profile
imprvement in unbalanced radial distribution systems (URDS). In
this paper Genetic Algorithm (GA) is used to obtain solution for
reconfiguration of radial distribution systems to minimize the losses.
A forward and backward algorithm is used to calculate load flows in
unbalanced distribution systems. By simulating the survival of the
fittest among the strings, the optimum string is searched by
randomized information exchange between strings by performing
crossover and mutation. Results have shown that proposed algorithm
has advantages over previous algorithms The proposed method is
effectively tested on 19 node and 25 node unbalanced radial
distribution systems.
Abstract: This paper proposes a neural network weights and
topology optimization using genetic evolution and the
backpropagation training algorithm. The proposed crossover and
mutation operators aims to adapt the networks architectures and
weights during the evolution process. Through a specific inheritance
procedure, the weights are transmitted from the parents to their
offsprings, which allows re-exploitation of the already trained
networks and hence the acceleration of the global convergence of the
algorithm. In the preprocessing phase, a new feature extraction
method is proposed based on Legendre moments with the Maximum
entropy principle MEP as a selection criterion. This allows a global
search space reduction in the design of the networks. The proposed
method has been applied and tested on the well known MNIST
database of handwritten digits.
Abstract: A hybrid learning automata-genetic algorithm (HLGA) is proposed to solve QoS routing optimization problem of next generation networks. The algorithm complements the advantages of the learning Automato Algorithm(LA) and Genetic Algorithm(GA). It firstly uses the good global search capability of LA to generate initial population needed by GA, then it uses GA to improve the Quality of Service(QoS) and acquiring the optimization tree through new algorithms for crossover and mutation operators which are an NP-Complete problem. In the proposed algorithm, the connectivity matrix of edges is used for genotype representation. Some novel heuristics are also proposed for mutation, crossover, and creation of random individuals. We evaluate the performance and efficiency of the proposed HLGA-based algorithm in comparison with other existing heuristic and GA-based algorithms by the result of simulation. Simulation results demonstrate that this paper proposed algorithm not only has the fast calculating speed and high accuracy but also can improve the efficiency in Next Generation Networks QoS routing. The proposed algorithm has overcome all of the previous algorithms in the literature.
Abstract: Researchers have been applying artificial/ computational intelligence (AI/CI) methods to computer games. In this research field, further researchesare required to compare AI/CI methods with respect to each game application. In thispaper, we report our experimental result on the comparison of evolution strategy, genetic algorithm and their hybrids, applied to evolving controller agents for MarioAI. GA revealed its advantage in our experiment, whereas the expected ability of ES in exploiting (fine-tuning) solutions was not clearly observed. The blend crossover operator and the mutation operator of GA might contribute well to explore the vast search space.
Abstract: The genetic algorithm (GA) based solution techniques
are found suitable for optimization because of their ability of
simultaneous multidimensional search. Many GA-variants have been
tried in the past to solve optimal power flow (OPF), one of the
nonlinear problems of electric power system. The issues like
convergence speed and accuracy of the optimal solution obtained
after number of generations using GA techniques and handling
system constraints in OPF are subjects of discussion. The results
obtained for GA-Fuzzy OPF on various power systems have shown
faster convergence and lesser generation costs as compared to other
approaches. This paper presents an enhanced GA-Fuzzy OPF (EGAOPF)
using penalty factors to handle line flow constraints and load
bus voltage limits for both normal network and contingency case
with congestion. In addition to crossover and mutation rate
adaptation scheme that adapts crossover and mutation probabilities
for each generation based on fitness values of previous generations, a
block swap operator is also incorporated in proposed EGA-OPF. The
line flow limits and load bus voltage magnitude limits are handled by
incorporating line overflow and load voltage penalty factors
respectively in each chromosome fitness function. The effects of
different penalty factors settings are also analyzed under contingent
state.
Abstract: The particular interests of this paper is to explore if the simple Genetic Algorithms (GA) starts with population of only two individuals and applying different crossover technique over these parents to produced 104 children, each one has different attributes inherited from their parents; is better than starting with population of 100 individuals; and using only one type crossover (order crossover OX). For this reason we implement GA with 52 different crossover techniques; each one produce two children; which means 104 different children will be produced and this may discover more search space, also we implement classic GA with order crossover and many experiments were done over 3 Travel Salesman Problem (TSP) to find out which method is better, and according to the results we can say that GA with Multi-crossovers is much better.
Abstract: A modified Genetic Algorithm (GA) based optimal selection of parameters for Automatic Generation Control (AGC) of multi-area electric energy systems is proposed in this paper. Simulations on multi-area reheat thermal system with and without consideration of nonlinearity like governor dead band followed by 1% step load perturbation is performed to exemplify the optimum parameter search. In this proposed method, a modified Genetic Algorithm is proposed where one point crossover with modification is employed. Positional dependency in respect of crossing site helps to maintain diversity of search point as well as exploitation of already known optimum value. This makes a trade-off between exploration and exploitation of search space to find global optimum in less number of generations. The proposed GA along with decomposition technique as developed has been used to obtain the optimum megawatt frequency control of multi-area electric energy systems. Time-domain simulations are conducted with trapezoidal integration along with decomposition technique. The superiority of the proposed method over existing one is verified from simulations and comparisons.
Abstract: This paper presents a novel genetic algorithm, termed
the Optimum Individual Monogenetic Algorithm (OIMGA) and
describes its hardware implementation. As the monogenetic strategy
retains only the optimum individual, the memory requirement is
dramatically reduced and no crossover circuitry is needed, thereby
ensuring the requisite silicon area is kept to a minimum.
Consequently, depending on application requirements, OIMGA
allows the investigation of solutions that warrant either larger GA
populations or individuals of greater length. The results given in this
paper demonstrate that both the performance of OIMGA and its
convergence time are superior to those of existing hardware GA
implementations. Local convergence is achieved in OIMGA by
retaining elite individuals, while population diversity is ensured by
continually searching for the best individuals in fresh regions of the
search space.
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.
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: In this paper we have proposed a novel dynamic least cost multicast routing protocol using hybrid genetic algorithm for IP networks. Our protocol finds the multicast tree with minimum cost subject to delay, degree, and bandwidth constraints. The proposed protocol has the following features: i. Heuristic local search function has been devised and embedded with normal genetic operation to increase the speed and to get the optimized tree, ii. It is efficient to handle the dynamic situation arises due to either change in the multicast group membership or node / link failure, iii. Two different crossover and mutation probabilities have been used for maintaining the diversity of solution and quick convergence. The simulation results have shown that our proposed protocol generates dynamic multicast tree with lower cost. Results have also shown that the proposed algorithm has better convergence rate, better dynamic request success rate and less execution time than other existing algorithms. Effects of degree and delay constraints have also been analyzed for the multicast tree interns of search success rate.
Abstract: Society has grown to rely on Internet services, and the
number of Internet users increases every day. As more and more
users become connected to the network, the window of opportunity
for malicious users to do their damage becomes very great and
lucrative. The objective of this paper is to incorporate different
techniques into classier system to detect and classify intrusion from
normal network packet. Among several techniques, Steady State
Genetic-based Machine Leaning Algorithm (SSGBML) will be used
to detect intrusions. Where Steady State Genetic Algorithm (SSGA),
Simple Genetic Algorithm (SGA), Modified Genetic Algorithm and
Zeroth Level Classifier system are investigated in this research.
SSGA is used as a discovery mechanism instead of SGA. SGA
replaces all old rules with new produced rule preventing old good
rules from participating in the next rule generation. Zeroth Level
Classifier System is used to play the role of detector by matching
incoming environment message with classifiers to determine whether
the current message is normal or intrusion and receiving feedback
from environment. Finally, in order to attain the best results,
Modified SSGA will enhance our discovery engine by using Fuzzy
Logic to optimize crossover and mutation probability. The
experiments and evaluations of the proposed method were performed
with the KDD 99 intrusion detection dataset.