AI Evolutionary algos

1. Prepared by
Jeethan & Jun
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2.  Overview
 Evolutionary Algorithms (EA)
 EA’s v/s Traditional search
 Pseudo code
 Parameters
 Characteristics of EAs
 Types of Eas
 Advantages and disadvantages
 References
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3.  Search Problem
 Darwinian natural selection
 Evolutionary Algorithms are population-
based “generate-and-test” search algorithms
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5.  Evolutionary algorithms operate on a
population of potential solutions applying the
principle of survival of the fittest to produce
better approximations to a solution.
 A type of Guided Random Search Used for
optimization problems
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6.  search is performed in a parallel manner
 Provides a number of potential solutions to a
given problem.
 They are generally more straight forward to
apply
 The final choice is left to the user
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8.  Parameters of EAs may differ from one type
to another. Main parameters:
◦ Population size
◦ Maximum number of generations
◦ Elitism factor
◦ Mutation rate
◦ Cross-over rate
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9.  There are six main characteristics of EAs
◦ Representation
◦ Selection
◦ Recombination
◦ Mutation
◦ Fitness Function
◦ Survivor Decision
 Representation:
◦ How to define an individual
◦ The way to store the optimization parameters.
◦ Determined according to the problem.
◦ Different types:
 Binary representation
 Real-valued representation
 Lisp-S expression representation
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10.  Selection
◦ Selection determines, which individuals are chosen for
mating (recombination) and how many offspring each
selected individual produces.
◦ Parents are selected according to their fitness by means of
one of the following algorithms:
 Roulette wheel selection
 Truncation selection
 Recombination
◦ Determines how to combine the genes of selected parents
◦ Types is determined according to the representation :
 Bits of the genes
 Values of the genes
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11.  Mutation
◦ Change on a single gene of the individual
 Fitness Function
◦ Gives an intuition about how good the individual is.
 Survivor Decision
◦ Idea of survival of the best individuals. It is about
Elitism factor.
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12.  Genetic Algorithms(GA) – binary
strings
 Genetic Programming(GP) –
expression trees
 Evolutionary Strategies(ES) – real-
valued vectors
 Evolutionary Programming(EP) – finite
state machines

13. Evolutionary Algorithms
Genetic
Algorithms
Genetic
Programming
Evolutionary
Programming
Evolutionary
Strategies

14.  Optimum parameter – Random strategy
 Classified as global search heuristics
 Represented by byte arrays
 Two requirements
• Genetic representation
• Fitness function
 Condition principal

15.  Finding the best
path between
two points in
"Grid World"
 Creatures in
world:
◦ Occupy a single
cell
◦ Can move to
neighboring cells
 Goal: Travel
from the gray
cell to the green
cell in the
shortest number
of steps
Finish
Start

16.  Representation:
N=00, E=10,
S=11,W=01
00 10 10 00 10 11 10 10 00 00 10 00 10 10 00 01 00 10 00 10 00 10 10 00 10 11 10 10 00 00 10 00 10 10 00 01 00 10 00 10
10 10 00 10 11 10 00 00 01 00 00 10 00 01 00 01 00 10 10 10
00 00 10 10 00 10 10 11 10 11 10 00 10 00 00 00 01 00 10 00
p1 =
p2 =
p2 =
10 10 00 10 11 10 00 00 01 00 00 10 00 01 00 01 00 10 10 10
00 00 10 10 00 10 10 11 10 11 10 00 10 00 00 00 01 00 10 00
p2 =
p2 =
00 00 10 10 00 10 10 11 10 11 10 00 00 01 00 01 00 10 10 10
p1+2 =
p1 = 00 11 01 10 10 00 00 01 00 10 00 10 11 10 00 00 10 00 10 10
p1’ = 00 11 00 10 10 00 10 01 00 10 00 10 11 10 00 00 11 00 10 10
 Population
 Fitness
function
 Mutation
 Selection
 Cross over

17.  find the proper program
 simple problems – High computation power
 represented by expression trees
 mainly operate cross-over
 mutation only can be applied once

18.  no fixed representation
 Only use mutation operation
 child is determined in a way of mutation
 So, we can conclude that there are three
steps:
◦ Initialize population and calculate fitness values
◦ Mutate the parents and generate new population
◦ Calculate fitness values of new generation and
continue from the second step

19.  mutation is very critical
 main application areas:
◦ Cellular design problems.
◦ Constraint optimization
◦ Testing students’ code
◦ ......
 not widely used

20.  Mainly use the real-vectors as coding
representation
 Very flexible
 Representation: represent floating, real-
vector as well
 Selection: neighborhood method
◦ plus selection (both parent and child)
◦ comma selection (only parent)
 Fitness function: objective function values.

21.  recombination & mutation: use
additional parameters sigma
represent the mutation amount
 three recombination functions:
◦ Arithmetic mean of the parents
◦ Geometric mean of the parents
◦ Discrete cross-over method.
 There are many application areas
of the ES. Some of
them:Optimization of Road
Networks
◦ Local Minority Game
◦ Multi-Criterion Optimization
◦ .....
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 Advantages of Ea’s
• Large application domain
• Complex search problems
• Easy to work in parallel
• Robustness
 Disadvantages of Ea’s
• Adjustment of parameters (trial-and-error)
 No guarantee for finding optimal solutions in a finite amount of time

23.  https://www.youtube.com/watch?v=ejxfTy4lI
6I

24.  http://en.wikipedia.org/wiki/Evolutionary_algorithm
 http://www.geatbx.com/docu/algindex-02.html#TopOfPage
 http://www.faqs.org/faqs/ai-faq/genetic/part2/section-
3.html
 http://en.wikipedia.org/wiki/Genetic_programming
 http://alphard.ethz.ch/gerber/approx/default.html
 http://en.wikipedia.org/wiki/Evolutionary_programming
 http://en.wikipedia.org/wiki/Genetic_algorithm
 http://homepage.sunrise.ch/homepage/pglaus/gentore.htm
 http://www.ads.tuwien.ac.at/raidl/tspga/TSPGA.html
 http://en.wikipedia.org/wiki/Evolution_strategy
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25. ?
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