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Genetic algorithm

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Presentation is about genetic algorithms. Also it includes introduction to soft computing and hard computing. Hope it serves the purpose and be useful for reference.

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Genetic Algorithm Made By -Garima Singhal
Introduction ,[object Object]
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Soft Computing ,[object Object],[object Object],[object Object],[object Object]
Difference between Soft computing And Conventional Computing Hard Computing(Conventional) Soft Computing Requires precisely stated analytical model and a lot of computation time. Tolerant of imprecision, uncertainty and approximation. Based on binary logic, numerical analysis and crisp software. Based on fuzzy logic, neural networks and probabilistic reasoning. Requires programs to be written. Can evolve its own programs. Deterministic. Incorporates stochasticity. Strictly sequential. Allows parallel computations.
Current Applications of Soft Computing include ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Evolutionary Computation ,[object Object]
Contd… ,[object Object],[object Object],[object Object]
 
 
Genetic Algorithm Terminologies ,[object Object],Individual  Any possible solution   Population Group of all  individuals Search Space All possible solutions to the problem Chromosome Blueprint for an  individual Trait Possible aspect of an  individual Allele   Possible settings for a  trait Locus   The position of a  gene  on the  chromosome Genome Collection of all  chromosomes  for an  individual
Premise ,[object Object],[object Object],[object Object],[object Object],[object Object]
Outline of the Genetic Algorithm ,[object Object],[object Object],[object Object],[object Object]
Basic Operators of Genetic Algorithm ,[object Object],[object Object],[object Object]
Basic genetic algorithms ,[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Working Principle of Genetic Algorithm
Genetic Algorithms: Case Study ,[object Object]
[object Object],f ( x ) = 15 x – x 2
The fitness function and chromosome locations
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Roulette wheel selection ,[object Object]
Crossover operator ,[object Object],[object Object],[object Object],[object Object]
 
Mutation operator ,[object Object],[object Object],[object Object],[object Object]
 
 
Example Checkboard ,[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object]
[object Object]
The GA Cycle of Reproduction Population Fitness Evaluation Genetic Operators Selection(Mating Pool) Parents Offspring New Generation Manipulation Reproduction Decoded Strings
Issues for Genetic Algorithm practitioners ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Benefits Of Genetic Algorithms ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Genetic Algorithm Applications Domains Application Types Control Gas pipeline, pole balancing, missile evasion, pursuit Robotics Trajectory planning Signal Processing Filter design Game Playing Poker, checker, prisoner’s dilemma Scheduling Manufacturing facility, scheduling, resource allocation Design Semiconductor layout, aircraft design, keyboard configuration, communication networks Combinatorial Optimisation Set covering, travelling salesmen, routing, bin packing, graph coloring and partitioning
Any Questions
Bibliography ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
 

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Genetic algorithm

  • 1. Genetic Algorithm Made By -Garima Singhal
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  • 7. Difference between Soft computing And Conventional Computing Hard Computing(Conventional) Soft Computing Requires precisely stated analytical model and a lot of computation time. Tolerant of imprecision, uncertainty and approximation. Based on binary logic, numerical analysis and crisp software. Based on fuzzy logic, neural networks and probabilistic reasoning. Requires programs to be written. Can evolve its own programs. Deterministic. Incorporates stochasticity. Strictly sequential. Allows parallel computations.
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  • 19. Working Principle of Genetic Algorithm
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  • 22. The fitness function and chromosome locations
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  • 34. The GA Cycle of Reproduction Population Fitness Evaluation Genetic Operators Selection(Mating Pool) Parents Offspring New Generation Manipulation Reproduction Decoded Strings
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  • 37. Genetic Algorithm Applications Domains Application Types Control Gas pipeline, pole balancing, missile evasion, pursuit Robotics Trajectory planning Signal Processing Filter design Game Playing Poker, checker, prisoner’s dilemma Scheduling Manufacturing facility, scheduling, resource allocation Design Semiconductor layout, aircraft design, keyboard configuration, communication networks Combinatorial Optimisation Set covering, travelling salesmen, routing, bin packing, graph coloring and partitioning
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