This document outlines genetic programming, including an introduction describing it as emulating Darwinian evolution within computer programs. It describes the typical life cycle of genetic programming, including initializing a population, evaluating programs, selecting programs for reproduction, applying genetic operators like crossover and mutation, and repeating the process. It also discusses experimental setup parameters, genetic operators, fitness evaluation methods, and applications of genetic programming in areas like regression, classification, telecommunications, computer networks, finance, clinical domains, and more.

1. Introduction
Experimental Setup
Genetic Operators
Applications
GENETIC PROGRAMING
Muhammad Adil Raja
Roaming Researchers, Inc
Muhammad Adil Raja Genetic Programming

2. Introduction
Experimental Setup
Genetic Operators
Applications
OUTLINE
1 INTRODUCTION
2 EXPERIMENTAL SETUP
3 GENETIC OPERATORS
4 APPLICATIONS
Muhammad Adil Raja Genetic Programming

3. Introduction
Experimental Setup
Genetic Operators
Applications
OUTLINE
1 INTRODUCTION
2 EXPERIMENTAL SETUP
3 GENETIC OPERATORS
4 APPLICATIONS
Muhammad Adil Raja Genetic Programming

4. Introduction
Experimental Setup
Genetic Operators
Applications
OUTLINE
1 INTRODUCTION
2 EXPERIMENTAL SETUP
3 GENETIC OPERATORS
4 APPLICATIONS
Muhammad Adil Raja Genetic Programming

5. Introduction
Experimental Setup
Genetic Operators
Applications
OUTLINE
1 INTRODUCTION
2 EXPERIMENTAL SETUP
3 GENETIC OPERATORS
4 APPLICATIONS
Muhammad Adil Raja Genetic Programming

6. Introduction
Experimental Setup
Genetic Operators
Applications
INTRODUCTION TO GENETIC PROGRAMMING (GP)
Genetic Programming is a coarse emulation of Darwinian
Evolution.
The search space is composed of all the possible
computer programs.
Muhammad Adil Raja Genetic Programming

7. Introduction
Experimental Setup
Genetic Operators
Applications
GP Life Cycle: The Pseudo Code
1 Create an initial population of computer programs.
2 Evaluation.
3 Selection.
4 Reproduction.
5 Evaluation.
6 Replacement.
7 Continue from 3.
Muhammad Adil Raja Genetic Programming

8. Introduction
Experimental Setup
Genetic Operators
Applications
A TYPICAL GP BREEDING CYCLE
FIGURE:
Muhammad Adil Raja Genetic Programming

9. Introduction
Experimental Setup
Genetic Operators
Applications
TABLE: Fiddle Parameters of a Typical GP Experiment
Parameter Value
Initial Population Size 300
Initial Tree Depth 6
Selection LPP
Tournament Size 2
Genetic Operators Crossover and Subtree Mutation
Operators Probability Type Adaptive
Initial Operator probabilities 0.5 each
Survival Half Elitism
Generation Gap 1
Function Set plus, minus, multiply, divide,sin,
cos, log2, log10, loge, sqrt,
power, if
Terminal Set Random real-valued numbers
between 0.0 and 1.0. Integers (2–10) and
Network traffic parameters from Table.
Muhammad Adil Raja Genetic Programming

10. Introduction
Experimental Setup
Genetic Operators
Applications
SELECTION
Roulette Wheel Selection – Fitness Proportionate
Selection.
Tournament Selection.
Muhammad Adil Raja Genetic Programming

11. Introduction
Experimental Setup
Genetic Operators
Applications
CROSSOVER
log
log
tan z
+
y
x * (tan y + z ) log (x + yz )
z
*
x +
+
x
zy
*
*
x +
x *
y z
x * ( (x+yz) + z ) log (tan y)
tan
y
offsprings
parents
Functions
Terminals
subtrees selected randomly for crossover
FIGURE:
Muhammad Adil Raja Genetic Programming

12. Introduction
Experimental Setup
Genetic Operators
Applications
MUTATION
log
log
tan z
+
y
x * (tan y + z ) log (x + yz )
*
x +
x *
y z
parents
Functions
subtrees selected randomly for crossover
Muhammad Adil Raja Genetic Programming

13. Introduction
Experimental Setup
Genetic Operators
Applications
SURVIVAL
Elitism
Replacement
Muhammad Adil Raja Genetic Programming

14. Introduction
Experimental Setup
Genetic Operators
Applications
FITNESS EVALUATION
Mean squared error (MSE).
Chi squared error.
Scaled mean squared error.
Muhammad Adil Raja Genetic Programming

15. Introduction
Experimental Setup
Genetic Operators
Applications
APPLICATIONS
Applications are quite too many.
GP as a hammer.
A hammer that finds everything else as a nail.
Muhammad Adil Raja Genetic Programming

16. Introduction
Experimental Setup
Genetic Operators
Applications
APPLICATIONS
In regression and classification.
Regression or Classification of nonlinear problems.
In Telecommunications: Speech quality estimation.
In Computer Networks: Network coding.
In Finance: In evolving effective bidding strategies.
In Clinical: Cancer detectors, seizure detectors, mental
health diagnosis etc.
Muhammad Adil Raja Genetic Programming

17. Introduction
Experimental Setup
Genetic Operators
Applications
MORE APPLICATIONS
In evolving chess players.
In evolving antenna designs.
Evolvable hardware.
Muhammad Adil Raja Genetic Programming