<!--<TABLE>
	<TR>
		<TD>
			
		</TD>
		<TD>
			See Also:
		</TD>
	</TR>
	<TR>
		<TD COLSPAN=2>-->

<P ALIGN="center"><SPAN CLASS="title">Application of Genetic Programming to the "Snake Game"</SPAN>
<BR><SPAN CLASS="author">by <A HREF="mailto:tobin@ehlis.com">Tobin Ehlis</A>
<BR><A HREF="javascript:if(confirm('http://www.rexall.com/tobin  \n\n這個文件不能通過 Teleport Pro 取回, 因為 它被訪問于一個域或在它的起始地址邊界外部的路徑上.  \n\n你想從服務器打開它嗎?'))window.location='http://www.rexall.com/tobin'" tppabs="http://www.rexall.com/tobin">http://www.rexall.com/tobin</A></SPAN>

<P><B>Abstract:</B> This paper describes the evolution of a genetic program to optimize a problem featuring task prioritization 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.</P>

<H1>Introduction and Overview</H1>
<P>Artificial intelligence (AI) techniques have been proven highly successful at the problems of navigation, task prioritization, and problem avoidance.  Traditionally, humans have encoded rule-based AIs to create the behaviors necessary to allow an automaton to achieve a specific task or set of tasks.  Genetic programming (GP), however, has been proven to allow a computer to create human-competitive results.  Specifically, examples such as the wall-following robot (Koza 1992) and Pac Man