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# Demonstration1 G As

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Introduction to Genetic Algorithms

Introduction to Genetic Algorithms

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• ### Transcript

• 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 &quot;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