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Prepared by Jeethan & Jun
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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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Search Problem
Darwinian natural selection
Evolutionary Algorithms are population-based “generate-and-test” search algorithms
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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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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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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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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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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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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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Genetic Algorithms(GA) – binary strings
Genetic Programming(GP) – expression trees
Evolutionary Strategies(ES) – real-valued vectors
Evolutionary Programming(EP) – finite state machines
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Evolutionary Algorithms
Genetic Algorithms
Genetic Algorithms
Genetic Programming
Genetic Programming
Evolutionary ProgrammingEvolutionary Programming
Evolutionary Strategies
Evolutionary Strategies
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Optimum parameter – Random strategy Classified as global search heuristics Represented by byte arrays Two requirements• Genetic representation• Fitness function
Condition principal
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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
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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
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find the proper program simple problems – High computation power represented by expression trees mainly operate cross-over mutation only can be applied once
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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
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mutation is very critical main application areas:
◦ Cellular design problems.◦ Constraint optimization◦ Testing students’ code◦ ......
not widely used
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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.
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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
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https://www.youtube.com/watch?v=ejxfTy4lI6I
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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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