Genetic programming

Department of Information Technology 1Soft Computing (ITC4256 )
Dr. C.V. Suresh Babu
Professor
Department of IT
Hindustan Institute of Science & Technology
Genetic Programming

What is Genetic Programming?
• Genetic programming is a model of programming which uses the ideas (and some of
the terminology) of biological evolution to handle a complex problem. … Genetic
programming can be viewed as an extension of the genetic algorithm, a model for
testing and selecting the best choice among a set of results, each represented by a
string.

Outline of the Genetic Algorithm
• Randomly generate a set of possible solutions to a problem, representing each as a
fixed length character string
• Test each possible solution against the problem using a fitness function to evaluate
each solution
• Keep the best solutions, and use them to generate new possible solutions
• Repeat the previous two steps until either an acceptable solution is found, or until
the algorithm has iterated through a given number of cycles (generations)

Why Genetic Programming?
• “It is difficult, unnatural, and overly restrictive to attempt to represent
hierarchies of dynamically varying size and shape with fixed length
character strings.” “For many problems in machine learning and artificial
intelligence, the most natural representation for a solution is a
computer program.” [Koza, 1994]
• A parse tree is a good representation of a computer program for Genetic
Programming

Using Trees To Represent Computer Programs
(+ 2 3 (* X 7) (/ Y 5))
+
2 3 * /
X 7 5Y
Functions
Terminals

Genetic Operations
• Random Generation of the initial population of possible
solutions
• Mutation of promising solutions to create new possible
solutions
• Genetic Crossover of two promising solutions to create new
possible solutions

Randomly Generating Programs
• Randomly generate a program that takes two arguments
and uses basic arithmetic to return an answer
– Function set = {+, -, *, /}
– Terminal set = {integers, X, Y}
• Randomly select either a function or a terminal to
represent our program
• If a function was selected, recursively generate random
programs to act as arguments

(+ …)
+

(+ 2 …)
+
2

(+ 2 3 …)
+
2 3

(+ 2 3 (* …) …)
+
2 3 *

(+ 2 3 (* X 7) (/ …))
+
2 3 * /
X 7

(+ 2 3 (* X 7) (/ Y 5))
+
2 3 * /
X 7 5Y

Mutation
(+ 2 3 (* X 7) (/ Y 5))
+
2 3 * /
X 7 5Y

Mutation
(+ 2 3 (* X 7) (/ Y 5))
+
2 3 /
5Y
*
X 7
First pick a random node

Mutation
(+ 2 3 (+ (* 4 2) 3) (/ Y 5))
+
2 3 + /
* 3 5Y
Delete the node and its children,
and replace with a randomly
generated program
24

Crossover
(+ X (* 3 Y))
+
X *
3 Y
(- (/ 25 X) 7)
-
/ 7
25 X

Crossover
(+ X (* 3 Y))
+
X *
Pick a random node in each
program
3 Y
(- (/ 25 X) 7)
-
/ 7
25 X

Crossover
(+ X (* (/ 25 X) Y))
+
X *
Swap the two nodes
3
Y
(- 3 7)
-
/
7
25 X

What About Just Randomly
Generating Programs?
• Is Genetic Programming really better than just randomly creating new functions?
• Yes!
– Pete Angeline compared the result of evolving a tic-tac-toe algorithm for 200 generations, with a
population size of 1000 per generation, against 200,000 randomly generated algorithms
– The best evolved program was found to be significantly superior to the best randomly generated
program [Genetic Programming FAQ, 2002]
• The key lies in using a fitness measure to determine which functions survive to
reproduce in each generation

Building a Better Mouse
• Apply Genetic Programming to the problem of navigating a maze
• What are our terminal and function sets?
– Function Set =
{If-Movement-Blocked, While-Not-At-Cheese*}
– Terminal Set =
{Move-Forward, Turn-Left, Turn-Right}
* While-Not-At-Cheese will be used exclusively as the root node of the parse tree

How to get the starving mouse to the cheese?
Is there a better solution for this maze? How good is
this solution?
One possible solution:
While not at the cheese
If the way ahead is blocked
Turn left 90 degrees
Otherwise
Move forward
Turn right 90 degreesCheese?
Blocked?

A fitness function:
Cheese?
Blocked?
• Each function and terminal other than the
root node shall cost one unit to execute
• If the mouse spends more than 100 units,
it dies of hunger
• The fitness measure for a program is
determined be executing the program,
then squaring the sum of the total units
spent and the final distance from the exit
• A lower fitness measure is preferable to a
higher fitness measure
Our mouse will die 10 moves from the exit
after spending 100 units, so the fitness
measure for our program is 12100

While not at the cheese (12996)
Otherwise
Move forward one space
Turn right 90 degrees

Otherwise
Otherwise
Mutation:

Otherwise
Otherwise
Crossover:

While not at the cheese
Otherwise
Otherwise
(after 4202 generations, with 1000 programs per generation)
Is this better?
Fitness measure: 2809

Real World Applications
• Lockheed Martin Missiles and Space Co. - Near-Minimum-Time Spacecraft
Maneuvers [Howley, 96]
• GP applied to the problem of rest-to-rest reorientation maneuvers for satellites
• Optimal time solution is a vector of nonlinear differential equations, which are
difficult to solve
• An approximate solution is necessary for a real-time controller
• Results: Rest-to-Rest Maneuver Times (8 test cases)
– Optimal Solution: 287.93 seconds
– Expert Solution: 300.3 seconds
– GP Solution: 292.8 seconds

• Symbolic Regression
• Problem: Given a set of data points, find a mathematical model

• Neural Network Optimization [Zhang, Mühlenbein, 1993]
• Image Analysis [Poli, 1996a]
• Generation of a knowledge base for expert systems [Bojarczuk, Lopes,
Freitas, 2000]
• Fuzzy Logic Control [Akbarzadeh, Kumbla, Tunstel, Jamshidi, 2000]
• Hardware Evolution (Field-Programmable Gate Array) [Thompson, 1997]

Genetic programming

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