Application of genetic algorithm to an obstacle game

An obstacle game takes one to two-dimensional space in which three kinds of obstacles exist: walls, flying dragons and abysses. Said obstacles can be avoided by such character's actions as walking, crouching and jumping. The character has an ability to move to a total of four directions (upwards, downwards, left and right). While doing so, it can, but is not obliged to, jump or crouch. Although the only way to avoid walls is by not walking into them (bumping into one results in an instant loss), dragons and abysses can be dodged by jumping or crouching respectively. The goal of the game is to complete levels by reaching objectives in as few moves as possible. Genetic algorithm, applied to a mathematical optimization problem of an obstacle game, looks for an optimal or best available solution by evaluating solutions' fitness values by two criteria: character's distance to an objective and the amount of moves a given solution made. The first population is generated at random. Each individual is evaluated and, with a chance of its fitness value, selected for crossover. Fittest individuals have the highest chance of passing down their genetic information. Fittest parents and children form a population of a new generation. The algorithm is repeated for a set amount of generations. The objective of this work is to construct a Genetic algorithm, capable of efficiently solving an obstacle game