Keywords : Genetic algorithm


Improved Classification Performance of Support Vector Machine Technique Using the Genetic Algorithm

Omar Saber Qasim; Mustafa Ayham Abed Alhafedh

AL-Rafidain Journal of Computer Sciences and Mathematics, 2018, Volume 12, Issue 2, Pages 49-60
DOI: 10.33899/csmj.2018.163581

In this research, the genetic algorithm was proposed as a method to find the parameters of support vector machine, specifically the σ and c parameters for kernel and the hyperplane respectively. Based on the Least squares method, the fitness function was built in the genetic algorithm to find the optimal values of the parameters in the proposed method. The proposed method showed better and more efficient results than the classical method of support vector machine which adopts the default or random values of parameters σ and c in the classification of leukemia data.
 

Using the Genetic Algorithm in Developing a Method for Steganography

Nadia M. Mohammed

AL-Rafidain Journal of Computer Sciences and Mathematics, 2013, Volume 10, Issue 4, Pages 35-46
DOI: 10.33899/csmj.2013.163555

This paper has developed a method for hiding in images, as it was first encrypt the secret message chaoticlly using the chaotic encryption algorithm and secondly execute the steganography in two phases, the first divide the cover image (.BMP, .PNG) to a group of sections (Blocks) with the diagonal sequence and make hiding using the cell of the least Significant Bit (LSB) within (Bytes) of certain randomly, and then using the Genetic Algorithm (GA) and working at the expense of Peak Signal to Noise Ratio(PSNR)  for each section after the steganography and then get the best PSNR value of the optimal section (ie, a better distribution of the random sites). The second include a final for all sections (Blocks) depending on the results of the first stage and the best for a random distribution of sites (Bytes) according to the results of genetic algorithm.
Measuressuch as PSNR, BER, MSE and NC are used to prove the accuracy of the results and efficiency. The application implemented using Matlab 9.
 

Hybrid Genetic Algorithm with Filters to Image Enhancement

Baydaa S Bhnam

AL-Rafidain Journal of Computer Sciences and Mathematics, 2013, Volume 10, Issue 4, Pages 107-119
DOI: 10.33899/csmj.2013.163550

Image enhancement  is a useful and necessary part of image processing and its analysis. The quality of an image could be corrupted by different kinds of noises, added due to the undesired conditions or during the transmission.
In this paper, a Hybrid Genetic Algorithm with Filters (HGAF ) is suggested for the removing of impulse noise from digital images. The new suggested algorithm HGAF uses popular (mean , median and min-max filters) and other proposed filters as fitness function for it in order to design eight proposed genetic filters. These eight proposed genetic filters are applied on several gray images corrupted by two types of noise (salt-and-pepper and gaussian noises) with different levels for comparison and to show the effectiveness of them by using the Peak Signal to Noise Ratio (PSNR) and Root Mean Square Error (RMSE). Also, proposed two methods of parents selection to compare between them and types of crossovers and mutations that are used.
 

Application of the Genetic Algorithm in the Network Intrusion Detection System Using NSL-KDD Data

Naglaa B. Ibrahim; Hana M. Usman

AL-Rafidain Journal of Computer Sciences and Mathematics, 2013, Volume 10, Issue 2, Pages 145-158
DOI: 10.33899/csmj.2013.163491

With the development of the Internet, technological innovation and the availability of information emerged new computer security threats. The researchers are developing new systems known as Intrusion Detection Systems IDSs for detecting the known and unknown attacks.  IDS have  two approaches depending on the detecting theories: Misuse Detection and Anomaly Detection.
This paper aims to design and implement  a misuse  network intrusion detection system based on Genetic Algorithm. The efficiency of using GA for building IDS based on NSL-KDD is verified. For rules generation NSL-KDD Data Set is used which include, KDDTrain and KDDTest, 125973 and 22544 records respectively, each record  consists of 41 features and one class attribute for specifying   normal and abnormal connection (complete train and test data are used), In order to get rid of redundancy and inappropriate features Principal  Component Analysis (PCA) is used for selecting (5)  features.
Number of experiments have been done. The experimental results show that the proposed system based on GA and using PCA (for selecting five features)  on NSL-KDD able to speed up the process of intrusion detection and to minimize the CPU time cost and reducing time for training and testing, that the detection rate: 91.6%  and false alarm is: 0% and classification rate  (DoS 93.48 %), (Normal 99.52%) , (Probe 81.16%), (R2L 69.47%), (U2R 32.84%). C# programming language is used for system implementation.
 

Genetic Linear Averaging Algorithm for Zooming Digital Images

Baydaa Sulaiman Bahnam

AL-Rafidain Journal of Computer Sciences and Mathematics, 2013, Volume 10, Issue 2, Pages 203-218
DOI: 10.33899/csmj.2013.163495

In this paper, the hypernation of linear averaging algorithm for zooming images is achieved with genetic algorithm. It's applied on a number of samples of images that lack the indistinction of the outline and providing accurate images. The equation of linear averaging is utilized as an objective function in genetic algorithm using several types of crossovers and mutations. A compression among these types is accomplished using two measures (RMSE & PSNR) for evaluating the proposed algorithm. The ratio for zooming is twice as the original images. The accuracy and the efficiency of those images are RMSE = 6.6541 and PSNR = 31.6470 db. A MATLAB 7.10.0(R2010a) environment is used for the programming of proposed algorithm will all applied types of crossovers and mutations.
 

Compress Digital Image based on Genetic Meta-Heuristic algorithm

Fawziya Mahmood Ramo; Yaser Noor Al Deen

AL-Rafidain Journal of Computer Sciences and Mathematics, 2013, Volume 10, Issue 2, Pages 135-143
DOI: 10.33899/csmj.2013.163490

In this research a propose method has be used to compression Data of digital image  based on one of Meta Heuristic Algorithm. Genetic Meta Heuristic has been applied to obtain effective data and then performed compression operation using Vector Quantization.
The proposed algorithm has been applied (we called it GMH) on sample of images.Efficince measures has been performed to calculate the value of (PSNR,MSE and correlation coefficient and compression ration). The experiments show that the proposed algorithm achives high performance and produces 87% compression rate.
 
 

Use the Genetic Algorithm to Encode and Hide Gray Image Data

Raya Jassim Essa; Reham Jassim Essa; Inam Muhammad Sulaiman

AL-Rafidain Journal of Computer Sciences and Mathematics, 2013, Volume 10, Issue 1, Pages 169-182
DOI: 10.33899/csmj.2013.163450

With the development of means of communication, computer science and information exchange via electronic information networks an  urgent need emerged to find ways to save exchanged information. Encryption had a prominent role in this area. However, with the development of intrusion hackers become able to  access to information and change it. This showed the need to adopt more sophisticated technology and more confidentiality in order to preserve the information. So, it become famous to  use the system of coverage in which the sent the information being  invisible to anyone, through hiding it inside the sent media, such as audio, image, text, and video. 
This paper aims to apply the idea to hide image message, using the least significant bit  algorithm inside an image and encrypt it in a new way for encryption using a genetic algorithm. For the purpose of increasing security of the access of the letter it is being encrypted to hide the message before using the genetic algorithm to generate random numbers employed in the process of concealment, for the highest extent of randomness. This in turn increases the strength of encryption and concealment. The study has been able to achieve this by adopting the recommended approach in such cases
 

Three Proposed Hybrid Genetic Algorithms

Ban A. Mitras; Nada F. Hassan

AL-Rafidain Journal of Computer Sciences and Mathematics, 2013, Volume 10, Issue 1, Pages 53-64
DOI: 10.33899/csmj.2013.163424

Genetic Algorithm has been hybridized with classical optimization methods. Hybridization has been done in three approaches, by using conjugate gradient algorithm for Fletcher and Reeves, second by using steepest descent method and lastly by creation of initial population for genetic algorithm from one of conjugate gradient method, the numerical results were encouraging.

Analysis of Basic Compounds in a Network Intrusion Detection System using NSL-KDD Data

Naglaa Badi Ibrahim; Hana Muhammad Usman

AL-Rafidain Journal of Computer Sciences and Mathematics, 2013, Volume 10, Issue 1, Pages 251-261
DOI: 10.33899/csmj.2013.163456

The increasing of security attacks  and unauthorized intrusion have made network security one of the main  subjects that should be considered in present data communication environment. Intrusion detection system  is one of the suitable solutions to prevent and detect such attacks. This paper aims to design and implement a Network Intrusion Detection System (NIDS) based on genetic algorithm. In order to get rid of  redundancy and inappropriate features  principle component analysis  (PCA) is useful for selecting features. The complete NSL-KDD dataset is used  for training and testing data.
Number of different experiments have been done. The experimental results show that the proposed system based on GA and using PCA (for selecting five features)  on NSL-KDD able to speed up the process of intrusion detection and to minimize the CPU time cost and reducing time for training and testing. C# programming language is used for system implementation.
 

Detection of network anomaly based on hybrid intelligence techniques

Shahbaa I. Khaleel; Karam mohammed mahdi saleh

AL-Rafidain Journal of Computer Sciences and Mathematics, 2012, Volume 9, Issue 2, Pages 81-98
DOI: 10.33899/csmj.2012.163720

Artificial Intelligence could make the use of Intrusion Detection Systems a lot easier than it is today. As always, the hardest thing with learning Artificial Intelligence systems is to make them learn the right things. This research focuses on finding out how to make an Intrusion Detection Systems environment learn the preferences and work practices of a security officer, In this research hybrid intelligence system is designed and developed for network intrusion detection, where the research was presented four methods for network anomaly detection using clustering technology and dependence on artificial intelligence techniques, which include a Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to develop and improve the performance of intrusion detection system. The first method implemented by applying traditional clustering algorithm of KM in a way Kmeans on KDDcup99 data to detect attacks, in the way the second hybrid clustering algorithm HCA method was used where the Kmeans been hybridized with GA. In the third method PSO has been used. Depending on the third method the fourth method Modified PSO (MPSO) has been developed, This was the best method among the four methods used in this research.
 

Using Genetic Algorithm to Reduce the Noise Effect on Images

Baydaa S Bhnam

AL-Rafidain Journal of Computer Sciences and Mathematics, 2012, Volume 9, Issue 2, Pages 127-142
DOI: 10.33899/csmj.2012.163723

This paper deals with a problem that concentrates on the noise removal that the images are affected from different resources employing Genetic Algorithm with filters. To achieve the aims of the paper, six types of genetic filters are suggested for noise removal from the images. These suggested genetic filters depending on filters (mean, median, min and max) as an objective function for them.
These suggested genetic filters are applied on several real images contaminated by two types of noise with different levels for comparison and to show the effectiveness of them. The result show that The fifth genetic filter that depends on the median filter as an objective function and heuristic crossover and  adding and subtracting mutation, gives the best results with RMSE=15.7243 and PSNR=24.1646 for Lena.bmp image and with RMSE=8.6197 and PSNR=29.4210 for girl.png image when add 0.05 salt & paper noise.
 

Compression of Digital Image based on Hybrid Heuristic Algorithm

Fawziya Mahmood Ramo; Yaser Noor Al Deen

AL-Rafidain Journal of Computer Sciences and Mathematics, 2012, Volume 9, Issue 2, Pages 159-171
DOI: 10.33899/csmj.2012.163725

In this research paper a system has been proposed to be used in data compression of digital image  based on two hybrid intelligent Algorithms. In the first algorithm which is now as  Meta Heuristic Genetic Compression Algorithm (MGCA) the characteristic and features of GA and local search are used to compress digital image. The second algorithm is the (HMGTCA) Hybrid Meta Genetic and Tabu Compression Algorithm. Hybrid operation has been done between Meta Heuristic Genetic and Tabu search algorithm. The proposed algorithm has been applied on four  samples. Efficiencies measures has been performed pled to calculate the value of (PSNR, MSE, correlation coefficient, compression ration and calculate the performance time). The experiments showed that the proposed algorithm achieved high performance and produces PSNR=  34.
 

Design Genetic Algorithm To Find The Optimal Critical Path Network Project (GAOCPN)

Samaa Tlayea Azeez; Niam Al-Thanoon; Lamyaa Jasim Mohammed

AL-Rafidain Journal of Computer Sciences and Mathematics, 2012, Volume 9, Issue 1, Pages 187-210
DOI: 10.33899/csmj.2012.163696

The present study deals with using up-to-date intelligent techniques. We try to utilize the genetic algorithm efficiently and integrate it with the problem of study by designing and applying a genetic algorithm to find the optimal critical path of networks GAOCPN achieving many results, e.g., real time. Accuracy in representing the steps of project execution as a net of nodes and paths has a great role in the accuracy of program results GAOCPN written in C++ version 5.0 under Window. The program was applied on many networks, such as Al-Sarafiya Bridge networks, and the execution time and results were checked and compared with the execution time and results of traditional methods (dynamic programming) and Win_QSB program. The GAOCPN showed accuracy of results in a standard time. Sometimes, it showed optimal results better than those of the traditional methods and it showed results identical to Win_QSB but in standard time.
 

Embedding and Extracting the Watermark in Video Files using Intelligent Techniques

Shahba I. Khaleel

AL-Rafidain Journal of Computer Sciences and Mathematics, 2011, Volume 8, Issue 1, Pages 25-44
DOI: 10.33899/csmj.2011.163620

This research, design a watermarking system for embedding and extracting the watermark in video files. This research presented four-efficient and powerful ways to embed and extract the watermark, the extract which in turn characterized as blind since it does not need the original cover in the process of extracting the watermark. The first method established the mean and SVD, which relied on the conversion of Singular values decomposition SVD and calculate the average of the unique values which are resulting from the application of the SVD on each data frame of the video file that required for embedding by, and then this method has been improved by hybrid intelligence by using a genetic algorithm for embedding the watermark, which is a second method. And other two methods has been implemented in this research. The third and fourth methods use artificial neural networks to embed the watermark. Depending on the characteristics of the data frame we use elman and Jordan neural networks and we use genetic algorithm to generate the secret key. The four methods of watermark were efficient and robust against various attacks which was found by measuring the efficiency of the methods by calculating the values of the Peak Signal-to-Noise Ratio PSNR and the correlation coefficient Normalized Correlation Coefficient NCC.
 

Hybrid Technique Used for Straight Line Detection

Fawziya Mahmood Ramo; Nidhal Al-Assady; Khalil I. Al-Saif

AL-Rafidain Journal of Computer Sciences and Mathematics, 2011, Volume 8, Issue 1, Pages 89-103
DOI: 10.33899/csmj.2011.163611

Many techniques have been used in this research to detect straight line in digital image on the same samples. These techniques are:1-Hough Transform(HT), Develpoed Baron’s Method (DBM)and-Genetic Developed Baron’s Method(GDBM)
    First technique was applied as it, while the second technique was applied after performing some modification in its algorithm. The third technique hybrid DBM (second technique) with GA, after performing the three techniques the accuracy and execution time for each technique is calculated. The experiment show that the hybrid technique relatively fast and it achieves high performance. It produces (90%) detection rate. MATLAB language has been used in the implementation of this software.
 

Straight Lines Detection Based on GA with New Modification on Baron's Method

Fawziya Mahmood Ramo; Nidhal H. Al-Assady; Khalil I. Al-Saif

AL-Rafidain Journal of Computer Sciences and Mathematics, 2010, Volume 7, Issue 3, Pages 61-72
DOI: 10.33899/csmj.2010.163911

In view of development on feature extraction in digital image based on feature straight line, GA has been used in this paper after hybrid it with Baron's Method to detect straight line, some developments are performed on the Baron's Method and we called it Genetic Developed Baron's Method (GDBM). The proposed method has been applied in many of sample. The experiments show that the proposed hybrid method in this paper is achieves high performance and it produce 90% detection rate.
 

Using Genetic Algorithm to Estimate (RNA) Estimator

Ban Ahmed Mitras; Farah Saad Nashat

AL-Rafidain Journal of Computer Sciences and Mathematics, 2010, Volume 7, Issue 3, Pages 145-160
DOI: 10.33899/csmj.2010.163934

In this paper the genetic algorithm has been used to estimate the parameter θ which exist in Boltzmann Distribution which controls the structure of the Ribo Nucleic Acid (RNA). Two algorithms have been suggested. The first found the value of the estimator which maximizes the likelihood function of Boltzmann Distribution. The second minimized the generation constraint of Boltzmann Distribution by using the genetic algorithm. Matlab (7.0) has been used in writing the programs of  algorithms and achieved the following results: The maximum value for the likelihood estimator for Boltzmann Distribution appear at the value -4.1614 where the value of θ is 0.1457, and the minimum value for the Constraint Generation for Boltzmann Distribution appear at the value 0.951039101*17  where the value of θ is -4.4066.
 

Practical Comparison Between Genetic Algorithm and Clonal Selection Theory on KDD Dataset

Najlaa Badie Aldabagh; Mafaz Muhsin Khalil

AL-Rafidain Journal of Computer Sciences and Mathematics, 2010, Volume 7, Issue 3, Pages 121-140
DOI: 10.33899/csmj.2010.163917

This paper compares between two models: Common Genetic algorithm and  the new Clonal selection theory in the field of Intrusion Detection. Genetic algorithms (GA) which is a model of genetic evolution, while Clonal selection theory (CST) is from models of the natural immune system NIS,  the two models are from two different fields of Artificial Intelligence AI but they have portion of shared operations and objectives. The comparison to be done by applying the two models on some records of Knowledge Discovery and Data mining tools which is known by the name KDD data sets (its records the data of the interring packets to the computer system from the internet), to produce population ( in case of GA) or antibodies (in case of CST) can recognize these abnormal records.         
 
 

Hybridization of Genetic Algorithm with Neural Networks to Cipher English Texts

Radwan Y. Al-Jawadi; Raid R. Al-Naima

AL-Rafidain Journal of Computer Sciences and Mathematics, 2010, Volume 7, Issue 3, Pages 77-90
DOI: 10.33899/csmj.2010.163928

This research aims in the first stage to built a cipher system using hybrid Genetic Algorithm with single layer Neural network to prevent any data attack during the transition process , where the ASCII of the letters are used as inputs to the network and the random numbers are used as outputs to the network , then the weights will be constructed after the network training .
In the second stage a decipher process is used to restore the ciphered data by using the inverse of the genetic neural network , where the inverse of weights is used as a key for the decryption process .
Stream cipher method is used to input the data in the network during the ciphering stage. This suggested technique attained 100% success.
All the ciphering and deciphering processes are built under MATLAB ver.(7) .
 

Enhance Fingerprint Image by Using Genetic Algorithms

Ban Ahmed Mitras; Daliya Abdullah

AL-Rafidain Journal of Computer Sciences and Mathematics, 2009, Volume 6, Issue 3, Pages 57-73
DOI: 10.33899/csmj.2009.163838

In this paper, an algorithm is proposed to enhance fingerprint image which includes in one of its steps a smoothing process in which the suggested genetic algorithms by Mitras and Anwar in 2007 which used  image smooth filters in both spatial and frequency domain will be employed to know their efficiency in enhancing and regaining the damaged sides of the fingerprint image to remove two types of noise, first one deals with noise added to the image, and the second one the noise already found in the image. Then histogram technique is used to enhance the fingerprint image.
 

Segmentation of Brain Tumor Images Using Genetic Algorithms

Mohameed Nathem; Manar Y. Kashmola; Dhuha Basheer Abdullah

AL-Rafidain Journal of Computer Sciences and Mathematics, 2009, Volume 6, Issue 1, Pages 31-45
DOI: 10.33899/csmj.2009.163783

In this research, the brain images resulting from computerized tomography  (CT) have been used in order to determine the tumor area in the brain. the steps started by preprocessing operation to the image before inputting it to algorithm .the image was converted to binary image in order to segment the image, later on into equal segments ,then the correlation coefficient  was found among these segments ,these values used as fitness function in the genetic algorithm in order to different iate between segments ,the result of the genetic algorithm was segment numbers which will be merged to form the sub-images ,then continuing these steps till determining the tumor approximate location. Another approach of image segmentation has been used without using the genetic algorithm by choosing the segments though a certain condition not randomly. Satisfying results have been reached in both approaches, but in different execution times. In both approaches tumor location was determined approximately, as a result the genetic algorithm was succeeded in about 85% while the first algorithm determine 80% of tumor locations.
 

Priority Dispatching Rules for Virtual Manufacturing Using Genetic Algorithm

Akela M. Al-Atroshi; Abdulsatar M. Khudur; Sama Talee Azez Al-Aubaidy

AL-Rafidain Journal of Computer Sciences and Mathematics, 2008, Volume 5, Issue 1, Pages 139-164
DOI: 10.33899/csmj.2008.163956

The current research concentrates on designing and applying an intelligent information system by the use of (Oracle) language based Multi- Agents manufacturing process to produce a new product. Every agent (user) has its own roles and privileges. The research focuses on determining the delivery date through using genetic algorithms to simulate Shop floor and specify priorities for dispatching orders according to specific rules  which determine the lead time of the product. The importance of the research stems from designing software in c++  to simulate manufacturing processes in the genetic algorithm to realize the following :

Attain the best sequences in implementing jobs according to the required rules.
Decreasing the queuing time for products and their components in the production processes.
Perfect utilization of the available resources.

The results of the designed system application have revealed that the operations planning by the use of the GA philosophy will perform a great role in calculating the product's lead time at the manufacturing operations' stages. This role supports the VM philosophy in calculating the industrial part of the products lead time quickly. Also the application results have confirmed that the designed GA software efficiency depends upon the number of jobs available at the time of execution; whenever the number of jobs is bigger, the software execution efficiency is better.
 

Using the Genetic Algorithm in Images Matching

Nidal H. Al-Asadi; Ghosson S. Bashir

AL-Rafidain Journal of Computer Sciences and Mathematics, 2008, Volume 5, Issue 1, Pages 117-128
DOI: 10.33899/csmj.2008.163966

The proposes of the present work is to suggest the use of a purely Genetic Algorithm(GA) as a search technique for the global optimum estimates of the transformation parameters. Because Genetic Algorithms search optimal solutions from the entire solution space, they often can obtain reasonable solutions in all situations. The program is written in Matlab language.
 

Application of Polyalphabetic Substitution Cipher using Genetic Algorithm

Ghusoon Salim Basheer

AL-Rafidain Journal of Computer Sciences and Mathematics, 2008, Volume 5, Issue 1, Pages 57-68
DOI: 10.33899/csmj.2008.163949

Several Genetic Algorithms have been developed for applications of cryptography problem; the primary distinction among all of them being the G.A. used for decryption problem and obtains the plain text. In this paper a new approach is proposed using Genetic Algorithm with cryptography. G.A. is used to obtain a best secret key in polyalphabetic substitution cipher. This key will be used then for encryption and decryption with a high level of security. The program is written in Matlab language (6.5).
 

An Implementation of an Initial Scale in Solving Binary Knapsack Problem Using a Genetic Algorithm

Abbas Y. Al-Bayati; Nawar N. Qubat

AL-Rafidain Journal of Computer Sciences and Mathematics, 2007, Volume 4, Issue 2, Pages 43-57
DOI: 10.33899/csmj.2007.164015

In this paper, we used a new operation in a Genetic Algorithm for solving the binary Knapsack problem depending on it’s LP Relaxation solution after eliminating the fractional part of the non-binary values. The benefit is to make a filter to the initial random population from the farness of the optimal solution and unsuitable chromosomes. This good property will be fixed automatically in all generations in the Genetic Algorithm until reaching the optimal binary solution.
 

Cryptanalysis of Knapsack Cipher Using Genetic Algorithm

Subhi H. Hamdon; Najlaa B. Al-Dabbagh; Milad J. Saeed

AL-Rafidain Journal of Computer Sciences and Mathematics, 2007, Volume 4, Issue 2, Pages 125-136
DOI: 10.33899/csmj.2007.164031

This research offers a new method in Cryptanalysis of knapsack cipher. It focuses on the application of genetic algorithm as a modern way in solving complex problems (problems have a huge numbers of alternate solutions in appropriate time). One of these problems is knapsack problem which is considered one of the known problems in operation researches. Cryptanalysis is done by using a new algorithm that is different from known knapsack breaking algorithm. Genetic algorithm has recently been successfully applied to the cryptanalysis of ciphers, among them Substitution  ciphers and Transposition ciphers. This research deals with another type of ciphers called Public-key ciphers, that are high secure ciphers because they are based on NP-Complete problems.
 

Using genetic Algorithm in Task scheduling for Multiprocessing System

Asmaa Yaseen Hammo; Ghosun S. Basheer; Muna M.T. Jawhar

AL-Rafidain Journal of Computer Sciences and Mathematics, 2007, Volume 4, Issue 1, Pages 81-98
DOI: 10.33899/csmj.2007.164004

In this work we use Genetic Algorithm for best section to implement many independent tasks on multiprocessor systems. The chromosome represented by numbers of integer value, every value represents one of the processors in the system, we use the simple crossover to generate the next population, and we us the mutation of type  partial gen for mutation  which has a good role to improve results of scheduling, the program written by matlab (6.5).The results, after a small number of iterations, were very good .