Genetic Algorithms and Rule Induction Analysis
Data mining is a data analyzing process that analyzes the data from different aspects and summarizes it into useful information that can be used to increase revenue and cost cuts (Data Mining: What is Data Mining? 2012). Data mining has different levels of analyzing. Genetic algorithms and rule inductions are two of the six different levels of analysis. Genetic algorithms are techniques that use genetic mutation, combination, and natural selection to analysis data. Rule induction is a way of analyzing data by means of extraction. Some of the attributes that will be discussed concerning genetic algorithms and rule induction are the benefits, limitations, risks of each of the selected techniques, and practical examples of when each technique would be most effectively utilized by a health care organization. Benefits

Data mining would not be a great tool without the parts that it is composed of. Genetic algorithms ad rule inductions are parts of data mining and just happen to have great benefits. The advantages of genetic algorithms are that they can be easily transferred, easy to understand and practical, and different types of problems can be solved (Advantages and disadvantages of genetic algorithms, 2012). Genetic algorithms can be transferred to simulations and models that previously existed. They are easily understood because they do not demand a mathematical background. Some of the problems that can be solved using genetic algorithms are optimization, multiple solutions, and multi-dimensional. Some of the advantages of rule induction are that it enhances the thinking process, it is easily understood, and the possibility of new knowledge can be deduced. The thinking process can be a challenge for some but with rule inductions the thinking processed is enhanced by offering a wide array of ideas. In order to understand rule inductions, an expert does not have to be a knowledge engineer. The new knowledge that...

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Fundamentals of GeneticAlgorithms : AI Course Lecture 39 – 40, notes, slides
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Fundamentals of GeneticAlgorithms
Artificial Intelligence
Geneticalgorithms,
topics
:
Introduction,
search
optimization
algorithm; Evolutionary algorithm (EAs); GeneticAlgorithms (GAs) :
biological background, search space, working principles, basic geneticalgorithm, flow chart for Genetic programming; Encoding : binary
encoding,
value
encoding,
permutation
encoding,
and
tree
encoding; Operators of geneticalgorithm : reproduction or selection
- roulette wheel selection, Boltzmann selection; fitness function;
Crossover – one point crossover, two Point crossover, uniform
crossover, arithmetic, heuristic; Mutation - flip bit, boundary, nonuniform, uniform, Gaussian;
Basic geneticalgorithm -
solved
examples : maximize function f(x) = x2 and two bar pendulum.
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Fundamentals of GeneticAlgorithms
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Artificial Intelligence
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Topics
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(Lectures 39, 40
2 hours)
1. Introduction
Slides
03-15...

...Journal of Information Hiding and Multimedia Signal Processing Ubiquitous International Volume 1, Number 1, January 2010
A Secure Steganography Method based on GeneticAlgorithm
Shen Wang, Bian Yang and Xiamu Niu
School of Computer Science and Technology Harbin Institute of Technology 150080, Harbin, China shen.wang@ict.hit.edu.cn; bian.yang@ict.hit.edu.cn; xiamu.niu@hit.edu.cn
Received April 2009; revised August 2009
Abstract. With the extensive application of steganography, it is challenged by steganalysis. The most notable steganalysis algorithm is the RS attack which detects the steg-message by the statistic analysis of pixel values. To ensure the security against the RS analysis, we presents a new steganography based on geneticalgorithm in this paper. After embedding the secret message in LSB (least signiﬁcant bit) of the cover image, the pixel values of the steg-image are modiﬁed by the geneticalgorithm to keep their statistic characters. Thus, the existence of the secret message is hard to be detected by the RS analysis. Meanwhile, better visual quality can be achieved by the proposed algorithm. The experimental results demonstrate the proposed algorithm’s eﬀectiveness in resistance to steganalysis with better visual quality. Keywords: steganography; steganalysis; genetic...

...Introduction to GeneticAlgorithms
S.N.Sivanandam · S.N.Deepa
Introduction to GeneticAlgorithms
With 193 Figures and 13 Tables
Authors
S.N.Sivanandam Professor and Head Dept. of Computer Science and Engineering PSG College of Technology Coimbatore - 641 004 TN, India S.N.Deepa Ph.D Scholar Dept. of Computer Science and Engineering PSG College of Technology Coimbatore - 641 004 TN, India
Library of Congress Control Number: 2007930221
ISBN 978-3-540-73189-4 Springer Berlin Heidelberg New York
This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, speciﬁcally the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microﬁlm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable for prosecution under the German Copyright Law. Springer is a part of Springer Science+Business Media springer.com c Springer-Verlag Berlin Heidelberg 2008 The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a speciﬁc statement, that such names are exempt from the relevant protective laws and regulations...

...A FAST ELITIST MULTIOBJECTIVE GENETICALGORITHM: NSGA-II
ARAVIND SESHADRI
1. Multi-Objective Optimization Using NSGA-II NSGA ( [5]) is a popular non-domination based geneticalgorithm for multiobjective optimization. It is a very eﬀective algorithm but has been generally criticized for its computational complexity, lack of elitism and for choosing the optimal parameter value for sharing parameter σshare . A modiﬁed version, NSGAII ( [3]) was developed, which has a better sorting algorithm , incorporates elitism and no sharing parameter needs to be chosen a priori. NSGA-II is discussed in detail in this. 2. General Description of NSGA-II The population is initialized as usual. Once the population in initialized the population is sorted based on non-domination into each front. The ﬁrst front being completely non-dominant set in the current population and the second front being dominated by the individuals in the ﬁrst front only and the front goes so on. Each individual in the each front are assigned rank (ﬁtness) values or based on front in which they belong to. Individuals in ﬁrst front are given a ﬁtness value of 1 and individuals in second are assigned ﬁtness value as 2 and so on. In addition to ﬁtness value a new parameter called crowding distance is calculated for each individual. The crowding distance is a measure of how close an individual is to its neighbors. Large average...

...INDIAN INSTITUTE OF TECHNOLOGY BHUBANESWAR
Digital Signal Processing Lab Report 6
Submitted by Name Allam Levi Ratnakar B Suresh Roll Number 08EEB025 08EEB026
Problem:
Design a model for the plant h (z) =0.2600+0.9300z¯¹+0.2600z¯² using direct modeling (Adaptive Algorithm LMS/RMS). The channel is associated with the following functions.
where p(k) is the output of each of linear part of the channels
Theory:
The aim of this experiment is to create a model for a plant with given parameters using direct modeling for the given different functions.
Given
H (z) =0.2600+0.9300z¯¹+0.2600z¯²
Matlab code for the given problem:
Main program is given below whereas subroutines aregiven after this program ends. inp = randn(1,502); p = [0.26 0.93 0.26]; snr = input('enter snr \n'); x=rand(10,30); for i=1:10 for j=1:30 if x(i,j)>0.5 x(i,j)=1; else x(i,j)=0; end end
end for i=1:10 a(i,:)=x(i,1:10); b(i,:)=x(i,11:20); c(i,:)=x(i,21:30); end for i=1:10 wt(i,:)=[bin2deci(a(i,:)) bin2deci(b(i,:)) bin2deci(c(i,:))]; end for n=1:20 for i=1:10 for j=1:500 X(1:3) = inp(j:j+2); Y1(i,j) = X*p'; sp(j) = rms(X); noise(i,j) = sqrt(sp(j)*(10^(-snr/10))); Y2(i,j)=tanh(Y1(i,j)+noise(i,j)); Y3(i,j) = X*wt(i,:)'; er1(i,j) = Y2(i,j)-Y3(i,j); ers1(i,j)=er1(i,j)*er1(i,j); end ermax(i,:) = max(ers1(i,:)); MSE(i,:) = 0.5*db(sum(ers1(i,:))/ermax(i,:)); end T=[wt a b c MSE]; T1=sortrows(T,34);
T2=T1(1:8,:); wt1=T2(:,1:3); a1=T2(:,4:13); b1=T2(:,14:23);...

...based on the notion of trying to place N queens on an N x N grid, such that no queen will be able to capture any other queen. The N-queens problem is typical of many combinatorial problems, in that it is simple to state and relatively easy to solve for small N, but becomes difficult with a large N. There are few ways to solve the N-queens problem. Some of them are trying all the permutations, using backtracking methods, using reinforcement learning methods, and etc. In this project,geneticalgorithm will be used to solve this problem by using GAlib package.
GeneticAlgorithms are adaptive methods which may be used to solve search and optimization problems. They are based on the genetic processes of biological organisms. Over many generations, natural populations evolve according to the principles of natural selection and "survival of the fittest". By mimicking this process, geneticalgorithms are able to "evolve" solutions to real world problems, if they have been suitably encoded.
GeneticAlgorithms use a direct analogy of natural behavior. They work with a population of "individuals", each representing a possible solution to a given problem. Each individual is assigned a "fitness score" according to how good a solution to the problem it is. The highly fit individuals are given opportunities to "reproduce", by "cross breeding" with other...

...45:733-750, 1998 c 1998 by John Wiley & Sons, Inc. www.interscience.wiley.com
A Competitive GeneticAlgorithm for Resource-Constrained Project Scheduling
S¨ nke Hartmann∗ o Institut f¨ r Betriebswirtschaftslehre u Lehrstuhl f¨ r Produktion und Logistik u Christian-Albrechts-Universit¨ t zu Kiel a 24098 Kiel, Germany hartmann @ bwl.uni-kiel.de www.bwl.uni-kiel.de/bwlinstitute/Prod/alumni/hartmann/hartmann.html
∗ supported
by the Studienstiftung des deutschen Volkes
Abstract In this paper we consider the resource-constrained project scheduling problem (RCPSP) with makespan minimization as objective. We propose a new geneticalgorithm approach to solve this problem. Subsequently, we compare it to two geneticalgorithm concepts from the literature. While our approach makes use of a permutation based genetic encoding that contains problem-speciﬁc knowledge, the other two procedures employ a priority value based and a priority rule based representation, respectively. Then we present the results of our thorough computational study for which standard sets of project instances have been used. The outcome reveals that our procedure is the most promising geneticalgorithm to solve the RCPSP. Finally, we show that our geneticalgorithm yields better results than several heuristic procedures presented in the...

...Question 1
In what follows we consider two simple algorithms for the Knapsack problem. We assume without
loss of generality, that for every object i we have si B (or else we can remove the object from the
set of objects).
Item A. Consider the following greedy algorithm for the knapsack problem: For each object i,
compute the “profit-to-size” ratio ri = pi/si. We order the objects according to ri, from big to small,
and then go over the objects in this order and add an object if it doesn’t violate the size constraint
(that is, the total size of the selected objects is at most B). Show that the approximation factor this
algorithm gives may grow (at least) linearly with B (so that in particular it is not any fixed constant
or even a function of the number of objects).
Item B. We modify the algorithm in the previous item as follows. We still order the objects according
to the ri’s. We then find the minimal k such that the total size of the first k objects according to this
order exceeds B. The algorithm now compares the total profit of the first k − 1 objects to the profit
of object k and takes the better of the two. Show that this algorithm gives an approximation factor
of 2.
Question 2
As mentioned in class, there is a more “natural” pseudo-polynomial algorithm for solving the Knapsack
problem using a dynamic programming approach. Specifically, for each 1 i...