The goal of the game is to solve puzzles with different levels of difficulty. To win, the player must transform a mutable row of colored shapes to a target row with the help of three operations (duplications, deletions and inversions). The rows of colored shapes represent sequences of genes, and the three possible operations represent biological events that are known to occur in real genomes. The final score of a puzzle corresponds to the total number of operations that were applied to the mutable row. This must be achieved with as few moves as possible (similar to golf). In the context of bioinformatics, this problem is called the genome sorting problem, which is to find the shortest sequence of events that can transform one sequence of genes into another.
DupLos and OrthoAlign are two examples of well-known heuristics used to solve the genome sorting problem. As this problem can be abstracted as a string matching problem, the algorithms need to find patterns when comparing two strings (genomes). We know that the human brain is very good to recognize patterns. So, every time a player solves a puzzle we can check if the player found a solution using less evolutionary events comparing with other heuristics. That approach will help us to develop better algorithms to solve the genome sorting problem.