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Demonstration1   G As
Demonstration1   G As
Demonstration1   G As
Demonstration1   G As
Demonstration1   G As
Demonstration1   G As
Demonstration1   G As
Demonstration1   G As
Demonstration1   G As
Demonstration1   G As
Demonstration1   G As
Demonstration1   G As
Demonstration1   G As
Demonstration1   G As
Demonstration1   G As
Demonstration1   G As
Demonstration1   G As
Demonstration1   G As
Demonstration1   G As
Demonstration1   G As
Demonstration1   G As
Demonstration1   G As
Demonstration1   G As
Demonstration1   G As
Demonstration1   G As
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Demonstration1 G As

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Introduction to Genetic Algorithms

Introduction to Genetic Algorithms

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    • 1. A Demonstration By: SAFI UR REHMAN PhD Scholar at Department of Mining Engineering GENETIC ALGORITHMS - A GENTLE INTRODUCTION
    • 2.
      • Definition of Genetic Algorithms
      • Basic Idea
        • Algorithms
        • Optimization
      • Background of Genetic Algorithms (GAs)
        • Natural world
        • Theory of Natural Selection
      • Introduction to GAs
        • Steps / Flowchart of GAs
      • Examples
      • Applications of GAs
      02/05/10 14:07 Genetic Algorithms – A gentle Introduction Overview
    • 3.
      • “ Genetic algorithms are stochastic search and optimization algorithms based on the mechanics of natural selection and natural genetics subject to survival of the fittest. The algorithm iteratively transforms a set (population) of mathematical objects (string structures), each with an associated fitness value, into a new population of offspring objects using crossover and mutation ”
          • Stochastic Search Algorithm
          • Natural Selection and Genetics Operators
          • Darwin’s Theory (Survival of the Fittest)
          • Evolutionary process
              • Population
              • Crossover
              • Mutation
      02/05/10 14:07 Genetic Algorithms – A gentle Introduction Definition of Genetic Algorithms
    • 4.
      • Suppose you have a problem
      • You don’t know how to solve it
      • What can you do?
      • Can you use a computer to somehow find a solution for you?
      • This would be nice! Can it be done?
      • A “BLIND GENERATE AND TEST” Algorithm:
      • Repeat
        • Generate a random possible solution.
        • Test the solution
        • and see how good it is
      • Until
      • solution is good enough
      02/05/10 14:07 Genetic Algorithms – A gentle Introduction x and y range from 1 – 10 Basic Idea
    • 5.
      • Can we used this dumb idea?
      • Sometimes - yes:
        • if there are only a few possible solutions
        • and you have enough time
        • then such a method could be used
      • For most problems - no:
        • many possible solutions
        • with no time to try them all
        • so this method can not be used
      02/05/10 14:07 Genetic Algorithms – A gentle Introduction if x and y range from 1 – 1000 Basic Idea
    • 6.
      • A “less-dumb” idea (GA)
      • Generate
      • a set of random solutions
      • Repeat
        • Test each solution in the set (rank them) Remove some bad solutions from set Duplicate some good solutions make small changes to some of them
      • Until
      • best solution is good enough
      02/05/10 14:07 Genetic Algorithms – A gentle Introduction Basic Idea
    • 7. local global I am not at the top. My high is better! I am at the top My Height is .. I will continue 02/05/10 14:07 Genetic Algorithms – A gentle Introduction Search for Optimization
    • 8. 02/05/10 14:07 Genetic Algorithms – A gentle Introduction Optimization
    • 9.
      • “ Genetic algorithms are stochastic search and optimization algorithms based on the mechanics of natural selection and natural genetics subject to survival of the fittest. The algorithm iteratively transforms a set (population) of mathematical objects (string structures), each with an associated fitness value, into a new population of offspring objects using crossover and mutation ”
      02/05/10 14:07 Genetic Algorithms – A gentle Introduction Definition of Genetic Algorithms
    • 10.
      • Genetic Algorithms
        • Developed by John Holland in 1975
        • Developed the theoretical basis of GAs through
        • Schema theorem
        • John Koza, David Goldberg, De Jong
      • Natural World Vs Function Optimization
        • Natural World
        • Diversity Complexity Useful features
          • Why it is so ?
          • How they come into being?
        • Can we imagine natural world as a result of many iterations in a grand optimization algorithm ?
      02/05/10 14:07 Genetic Algorithms – A gentle Introduction Background of GAs
    • 11. 02/05/10 14:07 Genetic Algorithms – A gentle Introduction
      • Genes are the basic “instructions” for building an organism
      • A chromosome is a sequence of genes
      • Biologists distinguish between an organism’s
      • genotype (the genes and chromosomes) and its
      • phenotype (what the organism actually is like)
      • Similarly, “genes” may describe a possible solution to a problem, without actually being the solution
      Natural World
    • 12. 02/05/10 14:07 Genetic Algorithms – A gentle Introduction
      • Human body is made up of trillions of cells. Each cell has a core structure (nucleus) that contains chromosomes.
      • Each chromosome is made up of tightly coiled strands of deoxyribonucleic acid (DNA). Genes are segments of DNA that determine specific traits, such as eye or hair color. A human have more than 20,000 genes.
      Natural World
    • 13.
      • In his book The Origin of Species Charles Darwin outlined the principle of natural selection.
      • IF there are organisms that reproduce, and
      • IF off springs inherit traits from their progenitors, and
      • IF there is variability of traits, and
      • IF the environment cannot support all members of a growing population,
      • THEN those members of the population with less-adaptive traits (determined by the environment) will die out, and
      • THEN those members with more-adaptive traits (determined by the environment) will thrive
      • The result is the evolution of species .
      02/05/10 14:07 Genetic Algorithms – A gentle Introduction “ Select The Best, Discard The Rest” - Survival of the Fittest Theory of Natural Selection
    • 14. 02/05/10 14:07 Genetic Algorithms – A gentle Introduction
      • Inspired by natural evolution
      • Population of individuals
        • Individual is feasible solution to problem
      • Each individual is characterized by a Fitness function
        • Higher fitness is better solution
      • Based on their fitness, parents are selected to reproduce offspring for a new generation
        • Fitter individuals have more chance to reproduce
        • New generation has same size as old generation; old generation dies
      • Offspring has combination of properties of two parents
      • If well designed, population will converge to optimal solution
      Introduction to Genetic Algorithms
    • 15.
      • “ Genetic algorithms are stochastic search and optimization algorithms based on the mechanics of natural selection and natural genetics subject to survival of the fittest. The algorithm iteratively transforms a set (population) of mathematical objects (string structures), each with an associated fitness value, into a new population of offspring objects using crossover and mutation ”
      02/05/10 14:07 Genetic Algorithms – A gentle Introduction Definition of Genetic Algorithms
    • 16. Randomly generate a population of potential solutions Evaluate fitness of population members Select two parents from population based on fitness Produce two children Evaluate children Crossover and mutation Is solution "Good“? Output best solution found Multiple Repeats in one iteration No Yes Genetic Algorithms Flowchart
    • 17. 02/05/10 14:07 Genetic Algorithms – A gentle Introduction Phenotype x Genotype g Gen. Phen. Mapping Population Objective Function f i Population Pop Cross over Mutation Genotype g Initial Population Create an initial population of random individuals
    • 18. 02/05/10 14:07 Genetic Algorithms – A gentle Introduction
      • Simple problem: max x 2 over {0,1,…,31}
      • GA approach:
        • Representation: binary code, e.g. 01101  13
        • Population size: 4
        • 1-point Crossover, bitwise mutation
        • Roulette wheel selection
        • Random initialization
      • One generational cycle will be shown
      A Simple Example of Genetic Algorithms 16 8 4 2 1 13 0 1 1 0 1 24 1 1 0 0 0 8 0 1 0 0 0
    • 19. 02/05/10 14:07 Genetic Algorithms – A gentle Introduction A Simple Example of Genetic Algorithms
    • 20. 02/05/10 14:07 Genetic Algorithms – A gentle Introduction 2 1 n 3 Area is Proportional to fitness value 4 Roulette Wheel Selection Individual i will have a probability to be chosen
    • 21. 02/05/10 14:07 Genetic Algorithms – A gentle Introduction 10011 101 11101 000 10011 000 Parent A Child of A and B Parent B Crossover Operator A Simple Example of Genetic Algorithms
    • 22. 02/05/10 14:07 Genetic Algorithms – A gentle Introduction A Simple Example of Genetic Algorithms
    • 23.
      • “ Genetic algorithms are stochastic search and optimization algorithms based on the mechanics of natural selection and natural genetics subject to survival of the fittest. The algorithm iteratively transforms a set (population) of mathematical objects (string structures), each with an associated fitness value, into a new population of offspring objects using crossover and mutation ”
      02/05/10 14:07 Genetic Algorithms – A gentle Introduction Definition of Genetic Algorithms
    • 24.
      • Function Optimization
      • Multi-Objective Optimization
      • Combinatorial Optimization
      • Economics and Finance
      • Resource Minimization
      • Scheduling
      • Robotics
      • Image Processing
      • Chemistry, Chemical Engineering
      • Networking and Communication
      • Constraint Satisfaction Problems (CSP)
      • Electrical Engineering and Circuit Design
      • Engineering, Structural Optimization, and Design
      02/05/10 14:07 Genetic Algorithms – A gentle Introduction Applications of Genetic Algorithms
    • 25. T H A N K S

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