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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