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Genetic and
Evolutionary Algorithms
Kevin Brown-Data Mining Methods-
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Presentation Outline
Discuss Evolutionary Algorithms
Brief History of Genetic Algorithms
Discuss Genetic Algorithms and itsProcess
View Pseudo-code
View Sources Q and A
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Evolutionary Algorithms
Pertains to Artificial Intelligence
Metaheuristic optimization algorithm
Subclass of EvolutionaryComputation
Most popular EA is the Genetic
Algorithm (GA)
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GA History
It all began with Nils Aall Carricelli(1954)
Biologists run amock with GA in the
60s Methods were published in the early
70s by Fraser, Burnell, and Crosby
Jon Holland brings GA to thespotlight with his work of the
mid70s
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Introduction to Genetic Algorithms
GA search technique used to findsolutions to optimization or
searchproblems
Categorized as a Global Search Heuristic A Class of EA that use
techniques inspired
by evolutionary biology
Applications include: comp sci.,engineering, mathematics,
physics, andeconomics
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GA Procedure
Population of individual solutionscreated
Each individual evaluated
Most fit are selected
The selected are then regrouped
New Population is formed Next algorithm iteration begins
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GA Initialization
Population of solutions randomlygenerated
Typically very large
Used to cover entire search range
Occasionally range is optimized
Discuss knapsack example
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GA Selection
Individuals are selected to reproduce
Fitness function weeds out the weak
The strong survive to reproduce Poor or weak solutions ruled
out
FF is stochastic
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GA Reproduction
A pair of parents selected
Parents create a child solution
Child shares attributes with parents Process repeats for
generations
Solutions evolve
End population much different fromthe first.
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GA Termination
Solution is satisfactory
Manual evaluation of results
Limited number of generations arefilled
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Pseudo-code
Choose initial population Evaluate the individual fitnesses of a
certain
proportion of the population Repeat:
-Select best-ranking individuals to reproduce-Breed new
generation through crossover andmutation (genetic operations) and
give birth tochildren-Evaluate the individual fitnesses of the
children
population-Replace best-ranking individuals
Until terminating condition(provided by Wikipedia.org)
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GA Restrictions
Cannot handle dynamic data
Convergence on optima dependanton fitness function
Cannot solve yes/no right/wrongproblems very well
In certain cases simpler algorithms
are better than GA GA produces good results in
complex data sets
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Sources
Genetic Algorithms:
http://en.wikipedia.org/wiki/Genetic_algorithm
Genetic Algorithms:Genetic Algorithms in Search,Optimization,
and Machine Learning byDavid E. Goldberg
Evolutionary
Algorithms:http://en.wikipedia.org/wiki/Evolutionary_algorithms
http://en.wikipedia.org/wiki/Genetic_algorithmhttp://en.wikipedia.org/wiki/Genetic_algorithmhttp://en.wikipedia.org/wiki/Genetic_algorithmhttp://en.wikipedia.org/wiki/Genetic_algorithm
