In every generation of the evolution, each solution individual (maze solver) is evaluated against all objective function candidates. We use the maximum fitness score obtained during the evaluation of a maze solver against each objective function candidate as a fitness score of the solution.
The fitness function of the maze solver is an aggregate of two metrics—the distance from the maze exit (the objective-based score) and the novelty of the solver's final position (the novelty score). These scores are arithmetically combined using a pair of coefficients obtained as an output from the particular individual in the objective function candidate's population.
The following formula gives the combination of these ...
