GA plus slicing tree is also widely developed in VLSI. Nallasamy mani, et al.1997 describes a combined genetic algorithm and slicing approach for floorplan area optimization during the early stage of integrated circuit design. It applies a partition procedure to reduce the complexity routing problem.

Genetic Algorithm (GA) is wide applied in almost any field, including solving FLP. Tam 1992 introduced the coding of layouts as a string of characters of finite length and used a fixed slicing tree structure defined by a clustering algorithm to represent a layout as a chromosome of string of characters. Its improvement is presented in Tam 1998 with a parallel GA approach in terms of schema coding and solution method. It relaxes the assumption of a fixed slicing tree structure by coding the structure, internal and external nodes of a tree as substrings in the schema. Based on the application limitation of the classical crossover and mutation operators of Tam 1998, L. Al-Hakim2000 introduced a preserving operation, referred to as transplanting, to produce feasible offspring. It also discusses the improvement of each of the GA development procedures with comparison with Tam1998. Though the use of GA has gained popularity with the application of slicing tree structure for layout problems, most implementations require repairing procedures to ensure the legality of the chromosome representations of the layout after application of genetic operators. To overcome this limitation, E. SHAYAN 2004 reported the design and development results of a new GA named GA.FLP.STS producing legal chromosomes without any need for repairing procedures. It introduced a penalty system to facilitate generating facilities with acceptable dimensions.

With the popularity and maturity of GA with slicing tree structure, more researches focus into the different aspects of FLP solutions. Kyu-Yeul Lee2002 and 2005 proposed a hybrid GA to derive solutions for facility layouts with inner walls...

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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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rs
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Fundamentals of GeneticAlgorithms
ha
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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...

...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);...

...Using of Porter Stremmer Algorithm
Overview
The Porter Stemmer is a conflation Stemmer developed by Martin Porter at the University of Cambridge in 1980. The stemmer is a context sensitive suffix removal algorithm. It is the most widely used of all the stemmers and implementations in many languages are available. This native functor creates a module that exports a function which performs stemming by means of the Porter stemming algorithm. Quoting Martin Porter himself:
The Porter stemming algorithm (or 'Porter stemmer') is a process for removing the commoner morphological and inflexional endings from words in English. Its main use is as part of a term normalisation process that is usually done when setting up Information Retrieval systems.
Algorithm
Porter's Algorithm works based on number of vowel characters, which are followed be a consonant character in the stem (Measure), must be greater than one for the rule to be applied. In details we can say that, every word (except noun) is a combination of consonant and vowel. A consonant is a letter other than A, E, I, O, U and Y preceded by a consonant. For example the in the word boy the consonants are B and Y, but in try they are T and R. A vowel is any letter that is not a consonant. A list of consonants greater than or equal to length one will be denoted by a C and a similar list of vowels by a V.Y preceded by a consonant here....

...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 extensiveapplication 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; geneticalgorithm; RS...

...
Workshop Five
“Essay Applications of genetic engineering”
By: Sandra Mendieta
Facilitator: Martha Perez
March 25, 2013
The technology advances every day with greater force, and the field of genetics is not far behind. The alteration of DNA in cells is increasingly used. There is talk of genetic changes to pharmacist level, plants and animals. In this essay I will make a brief summary of howgenetic engineering has been used in the creation of new techniques to heal incurable diseases such as cancer or AIDS, in the improvement of plants and animals.
Genetic Engineering is the method amending the hereditary characteristics of an organism in a predetermined way by altering its genetic material. Often used for certain microorganisms such as bacteria or viruses, increase the synthesis of compounds, forming new compounds, or adapt to different media. Other applications of this technique, also known as recombinant DNA technology, including gene therapy, providing a functioning gene to a person suffering from a genetic disorder or suffering from diseases such as acquired immunodeficiency syndrome (AIDS) or cancer.
Human gene therapy (TG) is the deliberate administration of genetic material to a human patient in an attempt to correct a specific genetic defect, that is, the insertion of...

...1 Introduction
The N-Queens problem is a classical AI problem. Its name is derived from the allowed moves for the queen piece in chess. Queens are allowed to move horizontally, vertically, or diagonally, backward and forward, with the only restriction being that they can move in only one direction at a time. A queen that can reach another piece in one move captures it.
The N-Queens problem is 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, genetic...

...Algorithms Homework – Fall 2000
8.1-1 Using Figure 8.1 as a model, illustrate the operation of PARTITION on the array A =
13 19 9 5 12 8 7 4 11 2 6 21
i j j
6 19 9 5 12 8 7 4 11 2 13 21
i i j j
6 2 9 5 12 8 7 4 11 19 13 21
i ………………………… j
return 11, SPLIT = and
8.1-2 What value of q does PARTITION return when all elements in the array A[p…r] have the same value?
q = (p+r)/2, where p = index 0, and r = highest index
8.1-3 Give a brief argument that the running time of PARTITION on a subarray of size n is (n).
In the worst case, PARTITION must move the j pointer by one element (to the 2nd to last element), and the i pointer all the way to j, making a comparison at each element along the way. Since there are n comparisons made, the running time is (n)
In the average (and best) case, PARTITION must move the j pointer to an element at or near the half-way point in the array and the i pointer all the way to j, making a comparison at each element along the way. Once again there are n comparisons made and the running time is (n)
8.2-1 Show that the running time of QUICKSORT is (n lg n) when all elements of array A have the same value.
T(n) =...

...GeneticAlgorithms 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. Geneticalgorithms and rule inductions are two of the six different levels of analysis. Geneticalgorithms 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 geneticalgorithms 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. Geneticalgorithms ad rule inductions are parts of data mining and just happen to have great benefits. The advantages of geneticalgorithms are that they can be easily transferred, easy to understand and practical, and different types of problems can be solved (Advantages and disadvantages of geneticalgorithms,...

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