1. Genetic Algorithms Abhishek Sharma-0691153004 Piyush Gupta -0651153004 Department of Instrumentation & Control

2. What are Genetic Algorithms? Genetic Algorithms (GAs)[1] are a global search method that emulates the process of natural evolution. John Holland formally introduced this method in the United States in the 1970 at the University of Michigan.

3. Description The genetic algorithm starts with no knowledge of the correct solution and depends entirely on responses from its environment. Work on the concept of global maxima & global minima.

4. Genetic Algorithm Process Flow Chart

5. Stages of a genetic algorithm:- Reproduction Crossover Mutation

6. Reproduction During the reproduction phase the fitness value of each chromosome is assessed. Just like in natural evolution, a fit chromosome has a higher probability of being selected for reproduction. An example of a common selection technique is the Roulette Wheel.

7. Crossover The crossover operations swaps certain parts of the two selected strings bid to capture the good parts of old chromosomes and create better new ones. Single Point & Multipoint crossover Illustration of Single Point Crossover Illustration of Multi Point Crossover

8. Mutation Using selection and crossover on their own will generate a large amount of different strings. There are two main problems with this: 1. Depending on the initial population chosen, there may not be enough diversity in the initial strings to ensure the Genetic Algorithm searches the entire problem space. 2. The Genetic Algorithm may converge on sub- optimum strings due to a bad choice of initial population.

9. These problems may be overcome by the introduction of a mutation operator into the Genetic Algorithm. Mutation is the occasional random alteration of a value of a string position. For example, if the GA chooses bit position 4 for mutation in the binary string 10000, the resulting string is 10010 as the fourth bit in the string is flipped.

10. Applications of Genetic Algorithms in Control Engineering PID Control Aircraft Control (Pitch, Roll, Yaw)

11. PID Control Using Genetic Algorithms PID controllers algorithm are mostly used in feedback loops. PID controllers can be implemented in many forms. [2]

14. Aircraft Pitch Control [3]

15. Conclusion An optimized approach to a problem results in a better operation.

16. References [1] An Introduction to Genetic Algorithms-MIT Press. [2] A Dissertation by SAIFUDIN BIN MOHAMED IBRAHIM University of New South Wales. [3] http://virtualskies.arc.nasa.gov/aerona utics/tutorial/motion.htmlhttp://virtualskies.arc.nasa.gov/aerona utics/tutorial/motion.html [4] University of Michigan.

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