Introduction
What is Genetic Programming?
Genetic Operators
Mutation
Crossover
How does it work?
What is Genetic Algorithm?
Genetic Algorithm
Sequence of steps
When to stop?
Mathematical Tools
Crossover Operation
Mutation
Symbolic Regression Problem
Advantages of GP
Advantages of GA
Application Fields of GP
Application Fields of GA
Our Example
Conclusion
A
genetic
programming
system
is
not
only
a
problem
solving
system
but
also
an
automatic
programming
system
.
We
provide
the
system
with
information
that
describes
what
we
want
a
program
to
do,
and
successful
run
of
the
system
produces
a
program
that
meets
the
desired
specification
.
We
must
also
provide
values
for
a
few
other
parameters,
but
most
of
this
is
straightforward
and
requires
little
expertise
about
the
problem
we
are
using
the
system
to
solve
.
In
this
study,
the
aim
is
to
using
Genetic
Programming
makes
easy
to
solve
problem
which
is
the
hardest
.
Also,
we
can
use
Genetic
Algorithm
which
is
the
kind
of
Genetic
Programming
for
solving
these
problems
.
Genetic
Programming
(GP)
is
a
method
of
solving
problems
using
computers
through
an
analogue
of
natural
selection
.
It
is
a
way
to
have
computers
automatically
solve
problems,
without
having
to
define
or
even
know
the
form
or
structure
of
the
optimal
solution
ahead
of
time
.
You
simply
provide
genetic
programming
with
basic
building
blocks
of
the
solution
and
some
method
of
analyzing
how
well
a
proposed
solution
solves
the
problem
and
GP
does
the
rest
.
Mutation
Mutation
affects
an
individual
in
the
population
.
It
can
replace
a
whole
node
in
the
selected
individual,
or
it
can
replace
just
the
node's
information
.
To
maintain
integrity,
operations
must
be
fail

safe
or
the
type
of
information
the
node
holds
must
be
taken
into
account
.
For
example,
mutation
must
be
aware
of
binary
operation
nodes,
or
the
operator
must
be
able
to
handle
missing
values
.
Crossover
Crossover
is
applied
on
an
individual
by
simply
switching
one
of
its
nodes
with
another
node
from
another
individual
in
the
population
.
With
a
tree

based
representation,
replacing
a
node
means
replacing
the
whole
branch
.
This
adds
greater
effectiveness
to
the
crossover
operator
.
The
expressions
resulting
from
crossover
are
very
much
different
from
their
initial
parents
.
Genetic
algorithms
are
a
very
effective
way
of
quickly
finding
reasonable
solution
to
a
complex
problem
.
Granted
they
aren't
instantaneous,
or
even
close,
but
they
do
an
excellent
job
of
searching
through
a
large
and
complex
search
space
.
Genetic
algorithms
are
most
effective
in
a
search
space
for
which
little
is
known
.
You
may
know
exactly
what
you
want
a
solution
to
do
but
have
no
idea
how
you
want
it
to
go
about
doing
it
.
This
is
where
genetic
algorithms
thrive
.
They
produce
solutions
that
solve
the
problem
in
ways
you
may
never
have
even
considered
.
Then
again
,
they
can
also
produce
solutions
that
only
work
within
the
test
environment
and
flounder
once
you
try
to
use
them
in
the
real
world
.
Put
simply
:
use
genetic
algorithms
for
everything
you
cannot
easily
do
with
another
algorithm
.
Sequence of
steps
1.
Input
: A generation of individuals.
2.
Calculation
of misfit and selection.
3.
Reproduction
(cross

over + mutation) = New population
4.
Go
to
(1)
with a new
generation
When
to stop?
Misfit
(a minimum
criteria
)
Number of generations (
maximum
reached
)
Allocated
budget reached (time,
money,
etc
.)
Solution
reached
a
plateau
Combinations
of
previous
criteria
Crossover Operation
Two
primary
operations
exist
for
modifying
structures
in
genetic
programming
.
The
most
important
one
is
the
crossover
operation
.
In
the
crossover
operation,
two
solutions
are
sexually
combined
to
form
two
new
solutions
or
offspring
.
The
parents
are
chosen
from
the
population
by
a
function
of
the
fitness
of
the
solutions
.
Three
methods
exist
for
selecting
the
solutions
for
the
crossover
operation
.
The
first
method
uses
probability
based
on
the
fitness
of
the
solution
.
If
is
the
fitness
of
the
solution
Si
and
Crossover Operation
is
the
total
sum
of
all
the
members
of
the
population
,
then
the
probability
that
the
solution
Si
will
be
copied
to
the
next
generation
is:
Another method for selecting the solution to be copied is
tournament selection. Finally, the last method is done by rank. In
rank selection, selection is based on the rank, of the fitness
values of the solutions of the population.
Mutation
Mutation is another
important feature of
genetic programming.
Two types of mutations
are possible. In the first
kind a function can only
replace a function or a
terminal can only
replace a terminal. In the
second kind an entire
subtree can replace
another subtree.
GP does not impose any fixed length of the solution.
In principle, the maximal length can be extended up
to the hardware limits.
GP does not require as much knowledge about the
problem and the possible solutions as do GAs. These
decisions are crucial for the power of GAs in solving a
problem and they usually require a lot of domain
knowledge.
Using GP, you can theoretically evolve any series of
actions a computer can possibly do, provided that
you give the GP algorithm a set of commands to
choose from that can describe all possible actions.
Advantages of Genetic Algorithm are the following:
They efficiently search the model space, so they are
more likely to converge toward a global minima.
There is no need of linearization of the problem.
There is no need to compute partial derivatives.
More probable models are sampled more frequently
than less probable ones.
Symbolic Regression and
Function
Synthesis
(Performing
symbolic
regression)
Grammar Induction
Data Mining and Data
Analysis
(
Data mining and
classification)
Electrical Engineering and
Circuit Design
(
Designing analog circuits)
Medicine
Economics and Finance
Geometry and Physics
Cellular Automata and Finite
State Machines
Automated Programming
Robotics
(Optimising robot control)
Networking and
Communication
(Evolving
sorting
networks
)
Evolving Behaviors for Agents
or
Game Players
Pattern Recognition
Biochemistry
Machine Learning
Discovery of quantum
algorithms
Image processing
Creating security protocols
Automotive Design
Engineering Design
Robotics
Evolvable Hardware
Optimized
Telecommunications Routing
Joke and Pun Generatio
Trip,Traffic and Shipment
Routing
Finance and Investment
Strategies
Biomimetic
Invention
Computer
Gaming
Encryption
and
Code
Breaking
Computer

Aided
Molecular
Design
Gene
Expression
Profiling
Optimizing
Chemical
Kinetic
Analysis
Marketing and Merchandising
Genetic programming is much more powerful than
genetic algorithms. The output of the genetic algorithm
is a quantity, while the output of the genetic
programming is a another computer program. In
actuality, this is the beginning of computer programs
that program themselves.
Genetic programming works best for several types of
problems. The first type is where there is no ideal solution,
(for example, a program that drives a car). In this case
genetic programming will find a solution that attempts to
compromise and be the most efficient solution from a
large list of variables.
Furthermore, genetic programming is useful in finding
solutions where the variables are constantly changing.
What is Genetic Programming,
http://www.geneticprogramming.us/What_is_Genetic_Programming.ht
ml
Lee Spector, 2004. Automatic Quantum Computer Programming,Kluwer
Academic
Publishers
Mutation, http://
en.wikipedia.org/wiki/Genetic_programming#mutation
Crossover,
http://
en.wikipedia.org/wiki/Genetic_programming#Crossover
Applications, http://
geneticalgorithms.ai

depot.com/Tutorial/Overview.html
Sequence of Steps,
http
://www.colorado.edu/geolsci/courses/GEOL6670/monsalve_GenAl
g.pdf
When to stop?,
http
://
www.colorado.edu/geolsci/courses/GEOL6670/monsalve_GenAl
g.pdf
Thomas Weise, 2009. Global Optimization Algorithms
–
Theory and
Application
David
R. White & Simon Poulding, 2009. A Rigorous Evaluation of
Crossover and
Mutation
in Genetic Programming, University of York,
UK
15 Real World Uses of Genetic Algorithms, http://brainz.org/15

real

world

applications

genetic

algorithms/
Koza, John R. 1992. Genetic Programming: On the Programming of
Computers by Means of Natural Selection. Cambridge, MA: The MIT
Press
Cramer, Nichael Lynn: "A Representation for the Adaptive
Generation of Simple Sequential Programs", Proceedings,
International Conference on Genetic Algorithms and their
Applications, July 1985 [CMU], pp183

187
Tree

based Genetic Programming,
http://en.wikipedia.org/wiki/Genetic_programming#Crossover
Genetic Programming,
http://www.ooop.org/publications/thesis/node8.html
Genetic Algorithm,
http://www.colorado.edu/geolsci/courses/GEOL6670/monsalve_Ge
nAlg.pdf
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