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Genetic and Evolutionary Algorithms

Explore the Evolutionary Algorithms, Genetic Algorithms, and their efficient processes through pseudo-code and historical context, highlighting applications in various fields like computer science and mathematics.

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Genetic and Evolutionary Algorithms

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  1. Genetic and Evolutionary Algorithms Kevin Brown -Data Mining Methods-

  2. Presentation Outline • Discuss Evolutionary Algorithms • Brief History of Genetic Algorithms • Discuss Genetic Algorithms and its Process • View Pseudo-code • View Sources • Q and A

  3. Evolutionary Algorithms • Pertains to Artificial Intelligence • Metaheuristic optimization algorithm • Subclass of Evolutionary Computation • Most popular EA is the Genetic Algorithm (GA)

  4. GA History • It all began with Nils Aall Carricelli (1954) • Biologists run amock with GA in the 60s • Methods were published in the early 70s by Fraser, Burnell, and Crosby • Jon Holland brings GA to the spotlight with his work of the mid 70s

  5. Introduction to Genetic Algorithms • GA – search technique used to find solutions to optimization or search problems • Categorized as a Global Search Heuristic • A Class of EA that use techniques inspired by evolutionary biology • Applications include: comp sci., engineering, mathematics, physics, and economics

  6. GA Procedure • Population of individual solutions created • Each individual evaluated • Most fit are selected • The selected are then regrouped • New Population is formed • Next algorithm iteration begins

  7. GA Initialization • Population of solutions randomly generated • Typically very large • Used to cover entire search range • Occasionally range is optimized • Discuss knapsack example

  8. GA Selection • Individuals are selected to reproduce • Fitness function weeds out the weak • The strong survive to reproduce • Poor or weak solutions ruled out • FF is stochastic

  9. GA Reproduction • A pair of parents selected • Parents create a child solution • Child shares attributes with parents • Process repeats for generations • Solutions evolve • End population much different from the first.

  10. GA Termination • Solution is satisfactory • Manual evaluation of results • Limited number of generations are filled

  11. Pseudo-code • Choose initial population • Evaluate the individual fitnesses of a certain proportion of the population • Repeat: -Select best-ranking individuals to reproduce -Breed new generation through crossover and mutation (genetic operations) and give birth to children -Evaluate the individual fitnesses of the children population -Replace best-ranking individuals • Until terminating condition (provided by Wikipedia.org)

  12. GA Restrictions • Cannot handle dynamic data • Convergence on optima dependant on fitness function • Cannot solve yes/no right/wrong problems very well • In certain cases simpler algorithms are better than GA • GA produces ‘good’ results in complex data sets

  13. Sources • Genetic Algorithms: http://en.wikipedia.org/wiki/Genetic_algorithm • Genetic Algorithms: Genetic Algorithms – in Search, Optimization, and Machine Learning by David E. Goldberg • Evolutionary Algorithms: http://en.wikipedia.org/wiki/Evolutionary_algorithms

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