lecture genetic-algorithms by chandrapro

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```									Evolutionary Systems and
Genetic Algorithms
Creative Systems (DESC9176)
Genetic Algorithms

“Genetic Algorithms are good at taking
large, potentially huge search spaces and
navigating them, looking for optimal
combinations of things, solutions you might
not otherwise find in a lifetime.”

- Salvatore Mangano
Computer Design, May 1995
From RB to Evolutionary
• In rule based:
– set of rules
– knowledge (start conditions)
• Set of solutions
• In evolutionary
– generation
– fitness
– iteration
Darwin’s Theory of Natural
Selection
• Two parents
– Generation
• Fitness
– Life and death
• English peppered moth

http://biology.clc.uc.edu/Courses/bi
o106/nat-sel.htm
Genetic Algorithms

Search techniques

Calculus-based techniques                           Guided random search techniques                 Enumerative techniques

Direct methods            Indirect methods        Evolutionary algorithms      Simulated annealing         Dynamic programming

Finonacci                    Newton               Evolutionary strategies    Genetic algorithms

Parallel                        Sequential

web.umr.edu/~ercal/387/slides/GATutorial.ppt
Evolution as search
•   Large space
•   Specify fitness
•   Means of generating solutions in space
•   Directed search of space
Generation
• Creation of new designs

• Parents breed:               Crossover
– Some from parent A some from parent B

• Anomalies:                   Mutation
– New value for a variable
– e.g. colour
Fitness
• Means of measuring performance of
individual
• Likelihood of reproduction
• Evaluation of behaviour variables
described in terms of structure
Pseudocode
{
initialize population;
evaluate population;
while TerminationCriteriaNotSatisfied
{
select parents for reproduction;
perform recombination and mutation;
evaluate population;
}
}
Evolved Plants (Sims 1991)
Evolving virtual creatures
• Karl Sims
http://www.youtu
be.com/watch?
v=F0OHycypS
G8
Examples
• Facial animation
– http://www.site.uottawa.ca/~wslee/publication/ISUVR2006.pdf
• Travelling salesman problem
– http://www.lalena.com/ai/tsp/
• Generating surfaces for Maya
– http://projects.csail.mit.edu/emergentDesign/genr8/index.html
• Robot controllers
– http://www.robertwrose.com/cg/learning/rose-evorobotics.pdf
• Generating textures
– http://www.csdl.tamu.edu/~mong/finalproj.htm
Almost eight years ago ... people at
Microsoft wrote a program [that] uses
some genetic things for finding short code
sequences. Windows 2.0 and 3.2, NT, and
almost all Microsoft applications products
have shipped with pieces of code created
by that system.”

Technology Group, Wired, September
1995
Applying a GA to a problem
• Finding structure variables
– Encoding genotype
• Finding behaviour variables
– Developing fitness function in terms of
genotype
• Define crossover and mutation
– How many parents?
– Random crossover?
– Chance of mutation?
Encoding a problem
• What are the structure variables?

• What do I need to be able to describe the
design?

• Example: number string, bitstring
Developing a fitness function
• What are the behaviour variables?

• How do I describe behaviour variables in
terms of the genotype?

• Example: assignation of points
Selection
• Better individuals get higher chance
• Chances proportional to fitness
Problems suited to GAs
Domain                  Application Types
Control                 gas pipeline, pole balancing, missile evasion, pursuit

Design                  semiconductor layout, aircraft design, keyboard
configuration, communication networks
Scheduling              manufacturing, facility scheduling, resource allocation

Robotics                trajectory planning

Machine Learning        designing neural networks, improving classification
algorithms, classifier systems
Signal Processing       filter design

Game Playing            poker, checkers, prisoner’s dilemma

Combinatorial           set covering, travelling salesman, routing, bin packing,
graph colouring and partitioning
Optimization
web.umr.edu/~ercal/387/slides/GATutorial.ppt

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