anglo pakistan

7/30/2019 Anglo pakistan

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CSC501

Dr. Hajira Jabeen

Advanced Design and Analysis

of Algorithms


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My Introduction

Ph.D. in August 2010

Expertise in

Artificial Intelligence

Machine Learning

Computational Intelligence

Data Mining

Classification

Clustering Evolutionary Computation

Swarm Intelligence


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Book Publication

Genetic Programming: A Novel tool forClassification

Issues and Advancements

Hajira Jabeen and Abdul Rauf BaigVerlag, Germany


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Journal Publications(sum of Impact 9)

Jabeen, H and Baig, A R., "DepthLimited Crossover in GeneticProgramming for Classifier Evolution." Computers in Human Behaviour,

Springer, 2010. (ISI Impact Factor 1.86)

Jabeen, H and Baig, A. R., GPSO: Optimization of Genetic

Programming Classifier Expressions for Binary Classification using

Particle Swarm Optimization. International Journal of Innovative

Computing, Information and Control, 2011. (ISI Impact Factor 2.93)

Jabeen, H and Baig, A. R., Two-Stage Learning for Multi-Class

Classification using Genetic Programming, Neurocomputing. (ISI

Impact Factor 1.44)

Jabeen, H and Baig, A. R., Two Layered Genetic Programming for

Mixed Variable Data Classification. Applied Soft Computing. (ISIImpact Factor 2.74)

Jabeen, H and Baig, A. R., Framework for Optimization of Genetic

Programming Evolved Arithmetic Classifier Expressions using Particle

Swarm Optimization for Multi-Class Classification., Knowledge Based

Systems. (ISI Impact Factor 1.57)


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Journals Jabeen, H and Baig, A R., "Review of Classification

using Genetic Programming." International Journal ofEngineering Science and Technology, Feb 2010,Issue 2, Vol. 2.

Jabeen, H and Baig, A R., "A Framework forOptimization of Genetic Programming EvolvedClassifier Expressions." Lecture Notes in ComputerScience, Springer, 2010.

Jabeen, Hand Baig, A R., "CLONAL-GP Framework

for Artificial Immune System Inspired GeneticProgramming for Classification." Lecture Notes inComputer Science, Springer, 2010.

Jabeen, Hand Baig, A. R., Multi-Class Classificationusing Genetic Programming. Lecture Notes in

Computer Science, Springer 2010.


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Conferences

Jalil, Z, Jabeen, H,Sponsorbased Architecture for Resource Management in MultiAgent Systems, IADISInternational Conference on Intelligent Systems and Agents 2007.

Jabeen, H, Jalil, Z and Baig, A R., "Opposition Based Initialization in Particle Swarm Optimization (O-PSO),Montreal, Canada, 2009, Genetic and Evolutionary Computation Conference (GECCO 2009).

Jabeen, Hand Baig, A R., "DepthLimited Crossover in Genetic Programming for ClassifierEvolution,Ulsan,South Korea, 2009, International Conference on Intelligent Computing (ICIC 2009).

Jabeen, H and Baig, A R., "Particle Swarm Optimization Based Tuning of Genetic Program EvolvedClassifier Expressions, Granada, Spain, 2010, International Workshop on Nature Inspired CooperativeStrategies for Optimization (NICSO 2010).

Jabeen, H and Baig, A R., "Framework For Optimization Of Genetic Programming Evolved ClassifierExpressions,Sansebastian, Spain,2010.Hybrid Artificial Intelligent Systems (HAIS 2010).

Jabeen, H and Baig, A R., "CLONAL-GP Framework for Artificial Immune System Inspired GeneticProgramming for Classification." Cardiff, UK, 2010. International Conference on Knowledge-Based andIntelligent Information & Engineering Systems (KES 2010).

Jabeen, H and Baig, A. R., Multi-Class Classification using Genetic Programming. Changsha, China,2010, International Conference on Intelligent Computing (ICIC 2010).

Jabeen, H and Baig, A. R., Lazy Learning for Multi-Class Classification using Genetic Programming .Changsha, China, 2010, International Conference on Intelligent Computing (ICIC 2010).

Imran, M, Jabeen, H, Ahmad, M, Abbas, Q, Bangyal, W and Abbas, Q Opposition based PSO and MutationOperators (OPSO with Power Mutation), Shanghai, China, 2010, International Conference on EducationTechnology and Computer (ICETC 2010).

Jalil, Z, Mirza,A, M, Jabeen, H,Word Length based Zero-Watermarking Algorithm for Tamper Detection inText Documents, Chengdu, China, 2010, International Conference on Computer Engineering andTechnology (ICCET 2010).


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International Seminar Data Classification using Genetic Programming at

School of Computer Science, Cardiff University, Sept,2010.

International

Presentations ICIC 2009, Ulsan, South Korea, Presentation of the

accepted research paper DepthLimited Crossover inGenetic Programming for Classifier Evolution.

KES 2010, Cardiff, UK, Presentation of the acceptedresearch paper CLONAL-GP Framework for ArtificialImmune System Inspired Genetic Programming forClassification.

ICIC 2011, ZhengZhou, China, Presentation of the

accepted research paperLazy Learning for Multi-Class Classification using Genetic Programming.


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International Journal Reviewer

Knowledge Based Systems, (ISI Impact Factor1.3), published by Springer.

International Conf. Reviewer Invited reviewer for4th IEEE International

Conference on Computer Science and

Information Technology, IEEE-ICCSIT-2011,

Chengdu, China.


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Teaching

FAST-NU, COMSATS, FJWU, Iqra University


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Introduction

Pre requisites Introduction to Algorithms course

Programming

Mathematics

I will not teach you

How to program / debug

Basic mathematics


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Grading Policy

Quizzes 10%

Assignments 5%

Midterm Exam 20 % Project 25%

Final 40%


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Quizzes

Unannounced

No makeup Quiz

Any miss will get 0


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Assignments

Submit a HARDCOPY before the class.

No late submissions

No email submissionsAny excuse will NOT be entertained

Any late will get 0


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Must put headings on your

submissions

Assignment number/project phase number

Your Correct ID Your name


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Project Target

Publish a Paper at the end of this course

At least know how to conduct research


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Course Material

Will be provided as we proceed with the contents

Course Group

http://groups.yahoo.com/group/ALG_IQRA/
http://groups.yahoo.com/group/ALG_IQRA/http://groups.yahoo.com/group/ALG_IQRA/


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Algorithm

A step by step procedure to solve a specificproblem in finite amount of time

Input-> algorithm ->output

Sorting problem

Input a set of numbers

Ouput list of sorted numbers


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Types by Implementation

Recursive

Logical

Serial/parallel/distributed

Deterministic/non deterministic Exact/Approximate


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Types by Design

Brute force

Divide and Conquer

Dynamic Programming

Greedy Linear

Reduction

Search

Heuristic


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Analysis of an Algorithm Determine the running time of a program as a

function of its inputs

Determine the total or maximum memory spaceneeded for program data;

Determine the total size of the program code; Determine whether the program correctly

computes the desired result;

Determine the complexity of the program--e.g.,

how easy is it to read, understand, and modify;and,

Determine the robustness of the program--e.g.,how well does it deal with unexpected or

erroneous inputs?


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Run time analysis

Run-time analysis is a theoretical classification

that estimates and anticipates the increase in

running time (or run-time) of an algorithm as its

input size (usually denoted as n) increases.

The number of (machine) instructions which a

program executes during its running time is called

its time complexity.


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Empirical Analysis

Algorithms are independent on Computer

Language

Operating system

It is difficult to analyze the running time of an

algorithm empirically.


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Time Complexity

Take as an example a program that looks up a

specific entry in a sorted list of size n.

Suppose this program were implemented onComputer A, a state-of-the-art machine, using

a linear search algorithm, and on Computer B,

a much slower machine, using a binary

search algorithm. Benchmark testing on thetwo computers running their respective

programs might look something like the

following:


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Order of growth

Landau symbols have mathematically precisedefinitions. They have many uses, such as in the

analysis of algorithms. These symbols are used

to evaluate and to concisely describe the

performance of an algorithm, in time and inspace.

O (called the Big Oh)

(upper case greek letterTHETA)


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BIG Oh, Landau notation, asymptotic

notation

The Big Oh is the upper bound of a function. Inthe case of algorithm analysis, we use it to bound

the worst-case running time, or the longest

running time possible foranyinput of size n. We

can say that the maximum running time of thealgorithm is in the order ofBig Oh.


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Big Oh

1 get a positive integer from input

2 ifn > 10

3 print "This might take a while..."

4 fori = 1 to n 5 forj = 1 to i

6 print i * j

7 print "Done!"

Instructions 1,2,3,7 will be executed once.

Evaluate execution of 4,5,6


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Mathematically speaking, O(n2) stands for a set offunctions, exactly for all those functions which, in

the long run, do not grow faster than the function

n2, that is for those functions for which the

function n2 is an upper bound (apart from aconstant factor.) To be precise, the following holds

true: A function f is an element of the set O(n2) if

there are a factor c and an integer number n0

such that for all n equal to or greater than this n0the following holds

f(n) cn2.


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ln(n)


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Big Oh Does Not Tell the Whole

Story (operations on data)


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Lower Bound

is also an order of growth but it is the oppositeof the Big Oh : it is the lower bound of a function.

We can say that the minimum running time of the

algorithm is in the order of.


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Good news / Bad news


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Hierarchy of complexities

Constant time // printing an inputLogarithmic time // binary search

Linear time // addition of input numbers

Quadratic time //sortingPolynomial time //

Exponential time // passwords

Factorial time // TSP

I t Si


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Input Sizes

n = 20 40

O(n2) 400 1600

O(2n) 1048576 1099511627776

O(n!) 2.4 x 1018 8.1 x 1047

Assume evaluating a solution takes 10-9 seconds

n = 20 40

O(n2) < 1 sec < 1 sec

O(2n) < 1 sec 1,100 sec

O(n!) 77 yrs 25 x 1018TRILLION yrs


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Space Complexity Analysis

Less time AND Less memory Exponential Memory Allocation