Analysis of Evolutionary Program Synthesis for Card Games

A genetic algorithm is a search heuristic that aims to find optimal solutions through ideas found in biology. This includes concepts such as survival of the fittest, mutations, crossbreeding, and more, in other words, an evolutionary approach. We previously saw in assignment 1, the performance of such an algorithm in a smaller version of the game CAN'T STOP, here we aim to evaluate the performance of it in the game RACK'O. Evolutionary approach is a method where the goal is to find a solution to a problem iteratively given a initial value. In the context of program synthesis, evolutionary approaches are used to generate a set of rules that is consistent with the game mechanics and this set of rules is expected to perform better than other scripts in the same search space. The set of rules that is obtained using a fitness function that measures how good the set of rules are given a state of the game. We chose to investigate the area of evolutionary approach in program synthesis because it is an interesting method to generate strategies and research using evolutionary approach spans over a multitude of program synthesis problems such as program sketching[1] and guided search for synthesizing programs with high complexity[2].