How was the project graded?

1. How was the project graded? • Yan Wang and Dr. Eick both graded the project; cases were our scores disagreed were discussed. • Originally different points were allocated to different part of the project and we used the so obtained score to obtain an initial ranking; after that we revised the initial ranking based on overall project quality. We also identified clusters of projects with somewhat similar quality, and adjusted points so that different quality groups are clearly separated . • Some minor grade modifications when I prepared these transparencies reading the project reports more carefully.

2. What was evaluated? • Is the program running? Does it produce correct solutions? Does it find solutions? • Analysis why a particular approach is doing well/badly? • Are results reported and do the reported results make sense? • Is the report clearly written and complete? Are experimental results presented using state of the art displays? • How did the program perform in the benchmark? What can be said about the quality of the obtained solutions? How do the different approaches tested compare? • Was there anything done to enhance the quality of the solutions? • Is the chosen approach sound? • Plus-points: Originality --- did the student propose something “unique” on his/her own? • How do the solutions of the program compare with those obtained by other students • Can the results be trusted. • Amount of work: total of successfully completed tasks/subtasks • If work by others is used; is it properly referenced? • If you cannot get anything running and obtain results, you likely will not get a very high score for the project.

3. TSP1 • Santamaria: • Steepest Decent Hill Climbing (Use swapping any two cities to generate neighborhood) • EC without crossover operator in which the best 50% remain in the population and the other 50% are replaced through mutation, also using a more complicated mutation operator (enhancements: used more sophisticated mutation operator, and better parameters for the EC system) • Murtuza: • Steepest Decent Hill Climbing swapping any two cities with randomized restart • EC with tournament selection and greedy crossover using a steady state model having 40% survive and the remaining 60% are replaced by crossover followed by mutation for 1/5 children (enhancements include: use greedy swap. )It takes EC less time to find good solutions when number of cities is large (e.g. 80) • EC finds better solution but it is slower; takes more than 10000 generations to find good solutions for large city sizes with population size 100.

4. TSP2 • Chun-Sheng Chen: • Steepest Decent Hill Climbing (…) • Ant Algorithm (ants can only do two things: 1) find the next destination 2) deposit pheromone on the path that is inversely proportional to the quality of the path; ants select the next path based on exploitation (amount of pheromone on the path) and exploration (try something new) • The ant-based algorithm does not perform to well due to high memory requirements and problems of selecting proper algorithm parameters • Korth: • … • Hill Climbing that somewhat greedily adds cities to a given path • Approach has some problems, particularly with cost function c3 where we would obtain 0-1-2-3-4-5 when 0-4-2-3-1-5 is much better…; the best continuation from 0 is the city with the largest number

5. TSP3 • Kurkure: • Steepest Decent Hill Climbing (Use swapping any two cities to generate neighborhood) • EC with crossover operator that copies part of route of one parent and cities in order they occur in the other parent; “greedy” mutation using route inversion (improvements: increased probility of mutation and copying, crossover operator does additional inversion, more greedy when selecting the next generation • Other results: tournament selection worked well (compared it with other approaches) • Ding: • Steepest Decent Hill Climbing (improvements to the original solution: using restart with a randomly selected starting position; using two mutation operators, swap and insertion-mutation, sweeping improvement of the neighborhood size??) • EC use PMX crossover operator, same two mutation operators (improvements: added restart; population size and tournament size is selected based on the number of cities,…) • It takes EC less time to find good solutions when number of cities is large (e.g. 80)

6. TSP4 • Bjork: • DFBH: proposes tree Traversal Approach using f-value cutoff similar to RBFS; use minimum spanning tree cost as heuristic function which is admissible (enhancements: more sophisticated rollback strategies, unwind to the nearest ancestor with a better f-value instead to the globally best ancestor) • Steepest Decent Hill Climbing (…) • DFBH did better for large number of cities but worse for smaller number of cities; DFBH did not do too well for cost function c2… • Vaezian: • Steepest Decent Hill Climbing swapping any two cities (improvement additionally reverse the path between the two nodes) • proposes minimum spanning tree approach (MST) relying on Prim’s algorithm; the original approach did not do well, if compared with hill climbing; problem: Prim’s algorithm is not guaranteed to find an optimal solution if cost function does not satisfy triangular inequality  20% off optimal solution

7. TSP5 • Dan Jiang: • Steepest Decent Hill Climbing swapping any two cities (improvements: add reverse operator) • Depth First Approach with Backtracking approach (stopping heuristic if the current path cannot be better than a previously found path; also proposes minimum spanning tree approach) • Feng: • Informed Depth-first search with some limited pruning • EC approach using Order1 algorithm for crossover and tournament selection and using two city swap as mutation • Informed Depth-first search is not able to search all solutions when the number of cities is above 15. • Informed Depth-first search outperformed evolutionary computing in the experiments

8. TSP6 • Paditar • Approach that starts with a circular tour involving n cities and extends by adding a city to a tour obtaining a circular tour of n+1 cities. • steepest Decent Hill Climbing that chooses the city that is the closest (improvement additionally reverse the path between the two nodes) • Schlak: • Also uses ant-like agents • Modifies SMA*

9. TSP7 • Baluch • Steepest Decent Hill Climbing with 2-change neighborhood • Parallel Nearest Neighbor with bound variable that abandons particular threads that are not competitive if compared with other threads. • Divide and Conquer Zoning Algorithm that decomposes the TSP problem into n/k smaller TSP problems and then tries to reconnect the locally found paths • Bui: • “Radius-based” steepest Decent Hill Climbing that chooses the city that is the closest (improvement additionally reverse the path between the two nodes) • EC-approach that uses genetic operators that center on replacing bad paths • Chen: • Steepest Decent Hill Climbing with 2-change neighborhood • Informed Search similar to other approaches mentioned earlier

10. 2004 6368 Project Evaluations • Most students found the Huntington Disease Project too time consuming and only 2/5 enjoyed it. Some students didn’t like the guessing part. I also believe that the instructions for the project should have outlined more clearly, how to approach the problem. Another problem was that many students started quite late running into the “usual” problems. • 4 students liked the reinforcement problems the most, but 2 students didn’t like it at all. • 2 students liked the decision-tree the most, and 2 student didn’t like it all. • The Traveling Salesman problem received good feedback; about 1/3 of the students found it too time-consuming • Students found most projects useful to learn about AI-technology (with the partial exception of the HD project and the Logical Reasoning Homework) • Not much feedback was given with respect of the other problems.