It is the purpose of this project to modify and extend the current genetic programming setup in hopes of discovering new approachs in implementation and representation that will facilitate the creation and evolution of GP players that can play well against both Edgar and the general othello playing population. In general, fitness over five games only is not precise, very noisy. This was having a population vote on each move. The new algorithm uses the number of players defeated as a fitness measure and ignores the number of pieces remaining on the board. Genetically engineered, the super dude creams Edgar, and many other random opponents called to compete against him.
Resistance is futile.
Resulting player from competitive run beats Edgar, and generalizes better than individuals trained directly against Edgar. Add new primitives, change fitness measure. Added two new primitives. The goal of this experiment is to test one method for increasing the learning rate and reducing the computation time in genetic programming applications. The intent of our addition to the genetic programming othello player was to allow the programs evolved to put an arbitrary weight on the features it was evaluating.