1. Neural Networks And Its Applications By Dr. Surya Chitra

2. OUTLINE Introduction & Software Basic Neural Network & Processing –Software Exercise Problem/Project Complementary Technologies –Genetic Algorithms –Fuzzy Logic Examples of Applications –Manufacturing –R&D –Sales & Marketing –Financial

3. OPTIMIZATION PROBLEM Combination of variables which produces the best results When No. of variables increase, it is difficult to do optimization. Genetic Algorithms serve as an alternative to optimization in feature selection

4. BIOLOGICAL EVOLUTION Evolution of species - “Struggle for Life” Best individuals have greatest probability of surviving and winning battles for reproduction. When a combination of two good genomes generates better genetic material. GAs are inspired by evolution theory.

5. Definition of Genetic Algorithms Genetic Algorithms are Search Algorithms Based on the Mechanics of Natural Selection and Natural Genetics Goldberg (1989) Genetic Algorithms are Software, Procedures Modeled After Genetics and Evolution Bauer (1993)

6. Genetics & Genetic Algorithms HUMAN CELL CHOMOSOMES (Dictate Hereditary of Individual) INDIVIDUAL GENES (Encodes Specific Feature Actual Value is Called Allele) GENETIC ALGORITHMS STRING STRUCTURES (Strings are Rated by Fitness Function) ELEMENTS in STRINGS Actual Value Stored in Elements) 23 Chromosomes New Strand of Chromosomes PARENTS OFFSPRING Crossover of strands Mutation of strands (diversification)

7. Basis for Genetic Algorithms Randomized Search –Strings are Chosen & Combined Stochastically Based on Survival of the Fittest –Uses Fitness Function Select Fittest String to Create New String Based on Interbreeding Population –To Create Innovative Search Strategy

8. EVOLUTIONS IN GA Experimental conditions leading to better results will prevail over the worst ones and an improvement can be obtained by a recombination with some random changes. Experimental Conditions -----> Genome Variables ---------> Genes Response ----------> Measure of fitness

9. BASIC STEPS IN GAs Coding of Variables Initiation of population Evaluation of the response –Reproduction –Crossover –Mutations Steps 3 to 6 alternate until a termination criterion is reached. (Lack of improvement or maximum number of generations)

10. Reproduction Reproduction allows individual strings to be copied for next generation. The chance that a string is copied depends on its fitness function.

11. Crossover Biologically it is the blending of Chromosomes. Selects two strings at random & calculates whether crossover should take place based on crossover probability.

12. MUTATIONS Mutations are irregular changes with very low probability of occurrence and affect single gene. Sometimes mutations generate good results and contribute to evolution.

13. Genetic Algorithm Iteration Loop

14. Example of GA Optimization Algorithm Assume: Answer is Integer and between 0 and 25

15. Example of GA Optimization Algorithm Example

16. Example of GA Optimization Algorithm First Iteration Selection

17. Example of GA Optimization Algorithm Mating Pool Crossover

18. Example of GA Optimization Algorithm End of First Iteration and New Population

19. Definition of Fuzzy Logic Systems Fuzzy Logic is a multi-valued logic that allows intermediate values to be defined between conventional evaluations like yes/no, true/false, etc. More Human-like way of thinking in the programming of computers. Initiated by Lotfi Zadeh Computer Science Univ. of California at Berkeley

20. Fuzzy Sets A = [ 5, 8 ]

21. Fuzzy Sets B = [ Set of Young People ] B = [ 0, 20 ]

22. Operations on Fuzzy Sets Fuzzy Set between 5 and 8 Fuzzy Set about 4

23. Operations on Fuzzy Sets Fuzzy Set between 5 and 8 AND about 4

24. Operations on Fuzzy Sets Fuzzy Set between 5 and 8 OR about 4

25. Operations on Fuzzy Sets Negation of Fuzzy Set between 5 and 8