genetic algorithms learning machines for knowledge discovery
Post on 15-Jan-2016
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Genetic AlgorithmsGenetic AlgorithmsLearning Machines for knowledge discovery
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Finding Patterns in DataFinding Patterns in Data Data mining is the task of
digging through this data looking for patterns, associations or predictions and which transform that raw material into useful information.
Evolutionary algorithms evolve the patterns which fit the data using Darwinian principles to weed out the patterns which don't work in favor of those that do. Survival of the fittest ensures that over time it is the patterns which best fit the raw data that are delivered as solutions.
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Concept HierarchyConcept Hierarchy All Knowledge Computer Science Artificial Intelligence Evolutionary Computation Evolutionary Algorithms
Genetic Algorithms Genetic Programming
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Human Knowledge
Computer Science
Graphics DatabasesArtificial Intelligence Networking
Natural Language Evolutionary Computation
Math Logic Language Physics Biology
Evolutionary Algorithms
Genetic Algorithms Genetic Programming
Swarm Intelligence
Expert Systems
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TerminologyTerminology Algorithm
A finite set of rules (a procedure) that solves a problem
Evolution A series of changes in a population over time affected
by biological, chemical, environmental, and technical factors
Evolutionary Algorithm An algorithm that uses selection, crossover and
mutation to produce better and better results
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Genetic AlgorithmsGenetic Algorithms The Genetic Algorithm is a model of machine learning
Based on the theory of evolution (Darwin) Accomplished by creating a population of individuals represented by “chromosomes” within a computer
Chromosomes can be just character strings that are analogous to the base-4 chromosomes that we see in our own DNA
The individuals in the population then go through a process of evolution (sexual reproduction followed by survival pressure on offspring)
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Evolutionary ForcesEvolutionary Forces Selection
A survival process Crossover
A sexual process Mutation
A random process
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SelectionSelection
Fitness to perform Mechanisms for Selection
Survival Quantitative function Human intervention
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CrossoverCrossover
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MutationMutation
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BiomorphsBiomorphs
Visualizing and controlling mutations
http://www.phy.syr.edu/courses/mirror/biomorph/
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Genetic ProgrammingGenetic Programming
Genetic programming is the application of genetic algorithms to computer programs themselves
Proposed byJohn Koza (Stanford)
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Genetic Programming ProcessGenetic Programming Process Start with a collection of functions randomly combine them into programs run the programs and see which gives the best
results keep the best programs (natural selection) mutate some of the others test the new generation repeat this process until a clear best program
emerges
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Genetic Programming ExampleGenetic Programming Example
Data (-1,1,3,5,7,9,11,13,15,17) Input function elements
x (can equal any digit 0..9) +,-,*,/
Starting functions (x,x+0,x*2,1+3,4/2)
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Function TreeFunction Tree
x *
x3
*
x2 *
x2
+
1 *
x2
-
1
0,1,2,3,4,5,6,7,8,9
0,3,6,8,12,15,18,21,24,27
0,2,4,6,8,10,12,14,16,18
0,3,5,7,9,11,13,15,17,19
-1,1,3,5,7,9,11,13,15,17
Many more functions
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Functional ValuesFunctional ValuesTruth x 3*x 2*x 2*x+1 2*x-1
-1 0 0 0 1 -11 1 3 2 3 13 2 6 4 5 35 3 9 6 7 57 4 12 8 9 79 5 15 10 11 9
11 6 18 12 13 1113 7 21 14 15 1315 8 24 16 17 1517 9 27 18 19 17
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FitnessFitnessFitness Fitness Fitness Fitness Fitness
1 1 1 2 00 2 1 2 0-1 3 1 2 0-2 4 1 2 0-3 5 1 2 0-4 6 1 2 0-5 7 1 2 0-6 8 1 2 0-7 9 1 2 0-8 10 1 2 0
SUM -35 55 10 20 0
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Genetic Algorithms are FlexibleGenetic Algorithms are Flexible
Can solve hard problems quickly and reliably.
Can be easily adapted to data (simulations, models)
Can be extended (scalable) Can be hybridized
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GA SoftwareGA Software Evolver (for Excel)
http://www.jurikres.com/catalog/ms_evol.htmPalisade Corp