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# Genetic Algorithms

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Overview and Applications of Genetic Algorithms

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### Genetic Algorithms

1. 1. ByDeepali Kundnani Shruti Railkar
2. 2.  Survival of the Fittest Natural selection Sir Charles Darwin
3. 3.  Chromosomes from two different parents  Chromatids from each overlap at Chiasma  Recombinant chromosomes are form  Further passed on to the progenyGenetic Crossover
4. 4. A T T G C T C ORIGINALA T A G C T C SUBSTITUTIONA T T G A C T C ADDITIONA T G C T C DELETION
5. 5. Offsprings have combinations of features inherited from each parentImage adapted from http://www.wpdipart.com
6. 6. Random changes are observedImage adapted from http://www.wpdipart.com
7. 7. Genetic Algorithm is a type of local search thatmimics evolution by taking a population of stringswhich encode possible solutions and combines thembased on a fitness function to produce individuals thatare more fit.
8. 8. 1) Encoding the two numbers into binary strings Parent 1=3.273672 =>11.0100011000001 Parent 2=3.173294 =>11.00101100010112) Randomly choose a crossover point; let suppose be it at bit 6, and we split the gene at position six. Parent 1=>3.273672=>11.010---0011000001 Parent 2=>3.173294=>11.001---01100010113) Swapping the two tails ends of binary strings. Child 1=>11.010---0110001011 Child 2=>11.001---00110000014) Recombining the two binary strings to get two new offspring. Child 1=>11.0100110001011 Child 2=>11.00100110000015) Decoding the binary strings back into floating point numbers. Child 1=3.298218 Child 2=3.148560
9. 9. Zeroth Generation
10. 10. First Generation
11. 11. 60th Generation
12. 12. 95th Generation
13. 13. 100th Generation
14. 14.  Artificial Intelligence Automotive Design Computer Gaming Predicting Protein Structure Optimization Problems Music Business
15. 15. Helps to determine theaccurate torsion anglesand predict proteinstructure
16. 16. Evolution of Monalisa : Roger Alsig Weblog
17. 17. Minimizing total error over the set of data points Source:http://www.geneticprogramming.org
18. 18. Musical examples of variations output to get perfect music. Fitness function determinant here is human ear
19. 19. Optimization of aerodynamics of a Car for a smooth drive on a crooked path Source: Youtube
20. 20. ADVANTAGES LIMITATIONS No training required  Do not work well when the population size is small and the rate of change is too high. Efficient even during Multi-modal or n-dimensional search space  If the fitness function is chosen poorly or defined vaguely, the Can work for non-linear Genetic Algorithm may be equations too unable to find a solution to the problem, or may end up Efficient solving the wrong problem
21. 21.  GAOT- Genetic Algorithm Optimization Toolbox in Matlab JGAP is a Genetic Algorithms and Genetic Programming component provided as a Java framework Generator is another popular and powerful software running on Microsoft Excel
22. 22.  Genetic Algorithm is related to “solving problems of everyday interest” in many diverse fields. However, several improvements can be made in order that Genetic Algorithm could be more generally applicable. Future work will continue through evolution and many more specific tasks
23. 23. Introduction to Genetic Algorithms -Axcelishttp://www.axcelis.com:80/articles/itga/application.html How Genetic Algorithm workshttp://www.mathworks.in/help/toolbox/gads/f6187.html Introduction to BioinformaticsBy Sundararajan & R. Balaji Functioning of a Genetic Algorithmhttp://www.rennard.org/alife/english/gavintrgb.html#gafunct