Abstract
This paper describes the evolution of a genetic
program to optimise a problem featuring task
prioritisation in a dynamic, randomly updated
environment. The specific problem approached is the
"snake game" in which a snake confined to a
rectangular board attempts to avoid the walls and its
own body while eating pieces of food. The problem is
particularly interesting because as the snake eats the
food, its body grows, causing the space through which
the snake can navigate to become more confined.
Furthermore, with each piece of food eaten, a new piece
of food is generated in a random location in the
playing field, adding an element of uncertainty to the
program. This paper will focus on the development and
analysis of a successful function set that will allow
the evolution of a genetic program that causes the
snake to eat the maximum possible pieces of food.
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