| Grade | Mean | Q75 |
|---|---|---|
| 18/20 (maximum) | 16.1 | 18 |
The project objective was to implement Genetic Algorithms to solve two well-know optimization problems: Travel Salesman Problem (TSP) and Portfolio Investment Problem (PIP). For TSP we did the design thinking - encoding rules, fitness function, admissibility function, etc. -, developed different approaches to reach an optimal solution (e.g. crossover, mutation, initialization and selection operators) and compared those approaches to find the best one. For PIP the focus was just on designing the problem and implementing Genetic Algorithms without trying to exhaustively find the optimal solution.
The code produced satisfies the OOP paradigm, which means that different instances of the TSP and PIP can be inputted and the program is still able to apply and run the Genetic Algorithm.
For the application of Genetic Algorithms to the TSP, we developed 4 Initialization approaches, 3 Selection approaches, 5 Crossover operators, 6 Mutation operators and 2 Replacement approaches. We ran over 2300 possible configurations by combining these approaches/ operators. We employed AWS instances to run some of these configurations.
The report which accompanies the code can be consulted at: https://tinyurl.com/cifo-tsp-pip.