Enhancing Decision Making Emulating Human Reasoning

1. Enhancing Decision Making Emulating Human Reasoning Matt Brunner Sts. Peter and Paul School – California, KY 5th-8th Grade Science & Math Samy Lafin Scott High School – Taylor Mill, KY 9th grade Clean Energy Engineering, 10th grade Biology RET is funded by the National Science Foundation, grant # EEC-1404766

2. Table of Contents • Abstract • Introduction • Background Literature Review • Goals and Objectives • Research Tasks • Timeline • Research Training Received • Unit Topic, Essential Question, Challenge • Progress Made

3. https://1.bp.blogspot.com/-4U-ehtZhNtg/U-DzNwDHFGI/AAAAAAAAEME/8eYYDB-oc2g/s1600/2014-08-chiwaukum-creek-fire.jpghttps://1.bp.blogspot.com/-4U-ehtZhNtg/U-DzNwDHFGI/AAAAAAAAEME/8eYYDB-oc2g/s1600/2014-08-chiwaukum-creek-fire.jpg

4. Introduction • Optimization • Genetic Algorithms • MATLAB • Traveling Salesman Problem

5. The Traveling Salesman Problem (TSP)1,5 http://lizardpoint.com/geography/images/maps/592x414xusa-caps-labeled.gif.pagespeed.ic.Cd8JRJohyU.png https://support.sas.com/documentation/cdl/en/ornoaug/65289/HTML/default/images/map002g.png

6. Optimization3 Real world problem Validation, sensitivity analysis Analysis Algorithm, model, solution technique Verification Numerical methods Computer implementation

7. Introduction to Genetic Algorithms2,4

8. Research Training Received 1 2 3 4 5 6 2 1 3 6 4 5 6 1 4 2 5 3

9. Research Training Received 1 2 3 4 5 6 6 4 5 2 1 3 6 4 5 1 2 3 2 1 3 4 5 6 6 1 4 2 5 3

10. How we use crossover 1 2 3 4 5 6 5 4 3 Parent 2 1 2 5 4 3 6 6 1 4 2 5 3

11. Two-Opt Algorithm

12. Abstract • Determine the shortest, most efficient route to TSP. • Develop a hybrid between GA and two-opt codes. • Eventual development of artificial intelligence https://static.securityintelligence.com/uploads/2016/01/Artificial-Intelligence-Heads-to-the-Enterprise-938x535.jpg

13. Objective • Develop a hybrid between GA and two-opt codes • Converges faster and/or find better solution

14. Research Tasks • Understand the functions and coding in MATLAB • Development of hybrid optimization program

15. Timeline • Week 1: Understand the function and coding in MATLAB • Week 2: Begin developing optimization techniques • Week 3: Develop hybrid algorithms • Week 4: Finish development, begin testing algorithms • Week 5: Test hybrid against two-opt and GA • Week 6: Finalize data and develop report

16. Research Training Received • MATLAB • Writing code to solve TSP

17. Progress Made

18. Verification of Code Green lines: Verification lines that MATLAB did not produce Red lines: MATLAB lines that were not found in verification

19. Progress Made Algorithm Additions • Modified Two-opt • Nearest Neighbor (GA) • Criss-cross • Ant Colony • N-opt • Circular, Quadrants • Looking at vectors instead of scalar numbers • Neural Network

20. Nearest Neighbor • GA Hybrid • Identify closest city

21. Nearest Neighbor with Crossover • GA Hybrid • NN only performed on select cities • Switches randomly between NN and flip

22. Modified Two-Opt • Tests 3 options

23. Experimental Data NO DATA

24. Distance Data

25. Time Data

26. Comparing Distances

27. Comparing Distances

28. Comparing Distances The basic data showed that the Nearest Neighbor hybrid with GA gave the best distance data.

29. Comparing Times

30. Comparing Times

31. Comparing Times The basic data showed that the Nearest Neighbor with Crossover hybrid with GA gave the slowest time, but it was difficult to discern any of the other tests.

32. Remove Nearest Neighbor w/ Crossover • Highest time – why? • Two-Opt algorithms faster • At 1000 cities, no data for Nearest Neighbor w/ Crossover

33. In the data for 1000 cities, it is clear that Nearest Neighbor with the genetic algorithm gives the best distance data, while the Two-Opt algorithm solves the problem in the least amount of time.

34. Conclusions • Nearest Neighbor: optimal distance • Two-Opt: most efficient • Application depends on situation • Forest Fire, GPS • Amazon shipping, pre-planning routes

35. Unit Topic, Essential Question, Challenge: Matt Brunner • Unit Topic: Plant Adaptations • Essential Question: What is the most effective method of grafting two or more cacti together? • Challenge: Connecting the vascular tissue of two different species of Opuntia Cactus.

36. Unit Topic, Essential Question, Challenge: Samy Lafin • Unit Topic: Human Body Systems – Organ Donation • Essential Question: How do we effectively and efficiently get donor organs to people waiting to receive a transplant? • Challenge: Develop a model of a system that could be used to determine organ donation between living donors and recipients that allows the most donors to donate and recipients to receive an organ.

37. Background Literature Review Applegate, D. L., Bixby, R. E., Chvatal, V., and Cook, W. J. (2007). The traveling salesman problem. Princeton University Press, Princeton. Carr, J. (2014). “An Introduction to Genetic Algorithms.” <https://karczmarczuk.users.greyc.fr/teach/iad/gendoc/carrgenet.pdf> (Jun. 22, 2016). Chinneck, J.W. (2000). “Chapter 1: Introduction.” Practical Optimization: a Gentle Introduction. Dianati, M., Song, I., and Treiber, M. (2002). An Introduction to Genetic Algorithms and Evolution Strategies. An Introduction to Genetic Algorithms and Evolution Strategies, tech., Canada. Matai, R., Singh, S., and Lal, M. (2010). “Traveling Salesman Problem: an Overview of Applications, Formulations, and Solution Approaches.” Traveling Salesman Problem, Theory and Applications. Petridis, V., Kazarlis, S., and Bakirtzis, A. (1998). “Varying fitness functions in genetic algorithm constrained optimization: the cutting stock and unit commitment problems.” IEEETrans. Syst., Man, Cybern. B IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 28(5), 629–640.

38. The RET program funded by the National Science Foundation, Grant ID# EEC-1404766 Project Faculty Mentors, Dr. Kelly Cohen and Dr. Jeff Kastner Graduate Research Assistant, Mr. Anoop Sathyan RET Project Director and Principal Investigator, Dr. Anant Kukreti RET Resource Person and Grant Coordinator, Debbie Liberi RET Resource Teacher, David Macmorine