Print ISSN: 1681-6900

Online ISSN: 2412-0758

Keywords : Genetic Algorithms

A Comparison between Single and Multi- Crossover Pointsto Break Hill Cipher Using Heuristic Search: MA & GA

Dalal A. Hammood

Engineering and Technology Journal, 2013, Volume 31, Issue 4, Pages 490-504

Hill cipher is a classical cipher which is based on linear algebra. In this method, matrices and matrix multiplication have been used to combine the plaintext.
Heuristic search is a search techniques. The methods of HS are: (GA, SE, EP, MA, TS). Genetic algorithms are one of Heuristic search, it is search techniques which is used natural selection. GAs select optimal solution through three operations, they are : selection, crossover and mutation. The parameters are kept in memory and the best values of fitness have been selected to represent next generation.
Memetic Algorithm is one of Heuristic search , a memetic algorithm is an extension of the traditional genetic algorithm. It uses a local search technique to reduce the likelihood of the convergence, to reach the best solution.
This paper focuses on using MA and GA to find optimal solution to cryptanalyse Hill cipher. Then comparing two methods of crossover to see which one has best solution, and comparing between GA and MA to see which one has best solution.
MATLAB is used as M-FILE.Theresults ofcryptanalysis cleared as following:-
1- Without genetic algorithms: The number of correct letters for the key was 1 out of 9.
2- Using genetic algorithms: two methods are used, and they have been compared of crossover, they are single and multi- crossover points randomly. After (250) generation, the number of correct letters was 4 out of 9 when single crossover point is used. The number of correct letters was 8 out of 9 when multi crossover point are used. So multi crossover point have best solution. Genetic algorithms are applied successfully.
3- Using Memetic Algorithms. After (100) generation, the number of correct letters was 8 out of 9. So MA is better than Genetic algorithms.
4- the number of correct letter was 9 out of 9 when the MA is used.

Tuning of Composite Fuzzy Logic Guidance Law Using Genetic Algorithms

Saadi A. Al-Obaidi; Munther N. Al-Tikriti; Ammar Gh. Al-Ghizi

Engineering and Technology Journal, 2012, Volume 30, Issue 13, Pages 2341-2356

The application of Fuzzy Logic (FL) for the development of guidance laws for
homing missile is presented. Fuzzy logic has been used to develop a Composite
Fuzzy Guidance (CFG) law. The objective of this proposed guidance law is to
combine desirable features of PN and APN homing guidance laws to enhance the
interception of targets performing uncertain maneuvers without reaching the missile
to saturation limit.
During this work, it became apparent that the fuzzy controller of the CFG law can
be further tuned to enhance its performance. Genetic Algorithms (GAs) which are
inspired by natural genetics are one of the algorithms that can be used to tune the
parameters of fuzzy controllers due to the promising results that they introduced in
the field of optimization.
This paper introduces the integration of GAs and FL with a main emphasis on
tuning the membership function parameters of fuzzy logic controller of the proposed
CFG law using Genetic Algorithms (GAs) with the view to improve its performance.
The simulation has been performed using Borland C++ programming language
(version 5.02) along with the Matlab programming package (version 7.0) that has
been used for plotting the results of simulations.

Optimization of liquid-liquid Extraction Column Using Genetic Algorithms

Ali D. Ali

Engineering and Technology Journal, 2012, Volume 30, Issue 10, Pages 1797-1810

In the present study, liquid-liquid extraction column was optimized using Genetic
Algorithms as a non-conventional optimization technique, which scores over
conventional techniques. Genetic Algorithm (GA) is a stochastic search technique
mimics the principle of natural genetics and natural selection to constitute search and
optimization. Genetic Algorithm is applied to the optimal design of liquid-liquid
extraction column to maximize the extraction rate using the superficial velocities of
raffinate and extract phases, (υx, υy) respectively as design variables using Matlab GA
toolbox. Different Genetic Algorithm strategies were used for optimization and the
design parameters such as Population size, crossover rate and Mutation were studied. It
was found that for constant distribution coefficient, m the convergence is obtained in a
very few generations (51 generations). The effect of distribution coefficient, m was
also studied on the optimization process and found that when increasing the
distribution coefficient the optimum extraction rate increased. The best values for υx
and υy were 0.142 and 0.059 respectively, and the objective function (maximum) was

Simple Learning Classifier Machine

Lubna.Z.Bashir; Hind .A.Alrazzaq

Engineering and Technology Journal, 2010, Volume 28, Issue 9, Pages 1862-1879

A learning classifier system is one of the methods for applying a genetic-based
approach to machine learning applications. An enhanced version of the system that
employs the Bucket-brigade algorithm to reward individuals in a chain of co-operating
rules is implemented and assigned the task of learning rules for classifying simple
objects. The task is to classify an object that has one or more of the following features:
wing, 2-legs/wheels, 3-legs/wheels, 4-legs/wheels, big, flies into one of the following:
bird, vehicle. the main goal is to exploit the ability of the algorithm to perform well in a
noisy environment and its ability to make little or no assumption about its problem
domain. Results are presented which show that the system was able to learn rules for
the task using only a few training examples and starting with classifiers that were
randomly generated. It is argued that a classifier based learning method requires little
training examples and that by its use of genetic algorithms to search for new plausible
rules, the method should be able to cope with changing conditions. Results show also
The parallel implementation of the algorithm would speed up the training process.

Study of Principle Component Analysis and Learning Vector Quantization Genetic Neural Networks

Arif A. Al-Qassar; Mazin Z. Othman

Engineering and Technology Journal, 2009, Volume 27, Issue 2, Pages 321-331

In this work, the Genetic Algorithm (GA) is used to improve the performance of
Learning Vector Quantization Neural Network (LVQ-NN), simulation results show that
the GA algorithm works well in pattern recognition field and it converges much faster
than conventional competitive algorithm. Signature recognition system using LVQ-NN
trained with the competitive algorithm or genetic algorithm is proposed. This scheme
utilizes invariant moments adopted for extracting feature vectors as a preprocessing of
patterns and a single layer neural network (LVQ-NN) for pattern classification. A very
good result has been achieved using GA in this system. Moreover, the Principle
Component Analysis Neural Network (PCA-NN) which its learning technique is
classified as unsupervised learning is also enhanced by hybridization with the genetic
algorithm. Three algorithms were used to train the PCA-NN. These are Generalized
Hebbian Algorithm (GHA), proposed Genetic Algorithm and proposed Hybrid
Neural/Genetic Algorithm (HNGA).