History
• Pioneered by John Holland in the 1970’s
• Got popular in the late 1980’s
• Based on ideas from Darwinian Evolution
• Can be used to solve a variety of problems
that are not easy to solve using other
techniques
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Genetic Algorithms-Genetics-Lecture Slides, Slides of Genetics
This lecture was delivered by Dr. Muhammad Hussain at Pakistan Institute of Engineering and Applied Sciences, Islamabad (PIEAS) for Genetics course. It includes: Genetic, Algorithms, Darwinian, Evolution, Population, Sexual, Recombination, Mutation, Engineering, Circles, Chromosomes
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2011/2012
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History
• Pioneered by John Holland in the 1970’s• Got popular in the late 1980’s• Based on ideas from Darwinian Evolution• Can be used to solve a variety of problems
that are not easy to solve using othertechniques
GENETIC ALGORITHM
The
genetic algorithm
is a probabalistic search algorithm
that iteratively transforms a set (called a
population
) of
mathematical
objects
(typically
fixed-length
binary
character strings), each with an associated fitness value,into a new population of offspring objects using theDarwinian
principle
of
natural
selection
and
using
operations that are patterned after naturally occurringgenetic
operations,
such
as
crossover
(sexual
recombination) and mutation.
Evolution in the real world
• Each
cell
of
a
living
thing
contains
chromosomes
- strings of
DNA.
• Each chromosome contains a set of
genes
blocks of DNA
• Each
gene
determines
some
aspect
of
the
organism (like eye colour)
• A collection of genes is sometimes called a
genotype
Evolution in the real world
• A collection of aspects (like eye colour) is
sometimes called a
phenotype.
• Reproduction
involves
recombination
of
genes from parents and then small amounts of mutation
(errors) in copying
• The
fitness
of an organism is how much it
can reproduce before it dies
• Evolution based on “survival of the fittest”
A dumb solution
A “blind generate and test” algorithm:
Repeat
Generate a random possible solutionTest the solution and see how good it is
Until solution is good enough
Can we use this dumb idea?
• Sometimes - yes:
– if there are only a few possible solutions– and you have enough time– then such a method
could
be used
• For most problems - no:
– many possible solutions– with no time to try them all– so this method
can not
be used
Simple Genetic Algorithm
{
initialize population;evaluate population;while TerminationCriteriaNotSatisfied{
select parents for reproduction;perform recombination and mutation;evaluate population;
} }
Evolution
•^
Here’s
a^
simple
description
of
how
evolution
works
in
biology.
- Organisms (animals or plants) produce a number of offspringwhich are almost, but not entirely, like themselves
Variation
may be due to mutation (random changes)
Variation may be
due to sexual reproduction (offspring have some characteristicsfrom each parent)
Can evolution be intelligent?
Intelligence can be defined as the capability of asystem to adapt
its
behavior
to
ever-changing
environment. According to Alan Turing, the formor
appearance
of
a
system
is
irrelevant
to
its
intelligence.
Evolutionary
computation
simulates
evolution
on
a computer. The result of such a simulation is a seriesof optimization algorithms, usually based on
a
simple
set
of
rules.
Optimization
iteratively
improves the quality of solutions until an optimal, orat least feasible, solution is found.
Can evolution be intelligent?
The
behavior
of
an
individual
organism
is
an
inductive inference about some yet unknown aspectsof its environment. If, over successive
generations,
the organism survives, we can say
that
this
organism is capable of learning to predict changesin its environment.
The
evolutionary
approach
is
based
on
computational
models
of
natural
selection
and
genetics. We call them evolutionary computation,an
umbrella
term
that
combines
genetic
algorithms,
evolution
strategies
and
genetic
programming.
Simulation of natural evolution
Neo-Darwinism
is based on processes of
reproduction,
mutation, competition and selection.
The
power
to
reproduce
appears
to
be
an
essential
property of life.
The power to mutate is also guaranteed in any living organismthat
reproduces
itself
in
a^
continuously
changing
environment.
Processes of competition and selection normally
take
place in the natural world, where expanding
populations of
different species are limited by a
finite space.
Simulation of natural evolution
Evolution
can
be
seen
as
a^
process
leading
to
the
maintenance
of
a^
population’s
ability
to
survive and
reproduce
in
a^
specific
environment.
This ability is
called
evolutionary fitness
Evolutionary
fitness
can
also
be
viewed
as
a
measure
of
the
organism’s
ability
to
anticipate
changes in its environment.
The
fitness,
or
the
quantitative
measure
of
the
ability
to
predict
environmental
changes
and
respond
adequately,
can
be
considered
as
the
quality that is optimized in natural life.
Genetic Algorithms
- An
algorithm
is a set of instructions that is repeated to solve a
problem.
- A
genetic algorithm
conceptually follows steps inspired by the
biological processes of evolution.
-^
Genetic Algorithms follow the idea of
SURVIVAL OF THE
FITTEST
- Better and better solutions evolve from previous
generations until a near optimal solution is obtained.
Genetic Algorithms
- Also
known
as
evolutionary algorithms
,^
genetic algorithms
demonstrate self organization and adaptation similar to the waythat the fittest biological organism survive and reproduce.
-^
A genetic algorithm is an iterative procedure that represents itscandidate solutions as strings of genes called chromosomes.
-^
Genetic Algorithms are often used to improve the performanceof other AI methods such as expert systems or neural networks.
-^
The method learns by producing offspring that are better andbetter as measured by a fitness function, which is a measure ofthe objective to be obtained (maximum or minimum).