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Evolve to solve presentation


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Evolve to Solve Presentation Diana Westerfer + Nathan Hyer

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Evolve to solve presentation

  1. 1. Team T0434-901 Individual Numbers: S4223 S4232 S4228 Evolve To Solve: A C# Problem Solving Framework Software Development Grapevine TX
  2. 2. Evolve To Solve | TSA STATES 2015 Genetic Algorithm Basics Genetic Algorithms have been used in a wide range of fields, from bioinformatics to economics to physics. Usually non-reusable, used in very specific circumstances for specific problems. Complex, requiring understanding of complicated programming practices Can be very slow and computer intensive Can solve complex, hard to understand problems Basic form of computer learning - The computer learns how to solve the problem. What does this all mean in regards to our project?
  3. 3. Evolve To Solve | TSA STATES 2015
  4. 4. What makes Evolve to Solve Better? ● Mobility ○ Evolve to Solve allows the use of Genetic Algorithms for any purpose the user wishes without requiring tedious rewrites. The framework is also small and lightweight, making it great for even memory-sensitive projects. Size of framework, post build: 11 kb ● Simplicity ○ Evolve to Solve is easy to implement, requiring only a limited knowledge of computer science, allowing students or those without much experience to explore the world of computer learning. While most genetic algorithms are slow and cumbersome, Evolve to Solve is quick and utilizes threading to prevent locking up Evolve To Solve | TSA STATES 2015
  5. 5. Proof of Concept Evolve To Solve | TSA STATES 2015
  6. 6. Proof of Concept As a proof of concept, our team used Evolve To Solve to try to generate copies of images using random changes. While less applicable to a real world problem, this example proves the concept of Evolve To Solve valid, even for problems with thousands of possible permutations. 10 Original Image: ETS 80% completion ETS Solution (takes about 10 seconds)
  7. 7. Example Problem Evolve To Solve | TSA STATES 2015
  8. 8. Evolve to Solve: Solving complex problems with computer learning Computer Learned Solutions Each was generated in only a few minutes. Scores range from 900 - 1,200 points. Humans scored 600 - 700 points All solutions were self- taught by the algorithm without the need for excess problem solving code. Evolve To Solve | TSA STATES 2015