Genetic Algorithms

What are we going to talk about?

• Evolution in biology• Evolution in programming• Components of a genetic algorithm• Strengths and limitations• Real world examples

Evolution in biology• Darwin

• Variation

• Selection

• Heredity

Evolution in biology• Modern evolution theory

• Discovery of DNA

• The gene as heredity and variation operator

Evolution in programming

Evolution in programming

ComponentExample

Component - Representation

Phenotype Genotype

Frame: 16 parameters

3 wheels: 9 parameters

“DNA” string of 25 values

8.7 4.7 9.5 5.9 6.2 1.3 … 9.3 1.0 8.7 3.8 1.7

Component - Population

Component - Fitness Function

Distance traveled

Component - Parent selection mechanism

0,1 0,5

0,4 0,1

Chance forreproduction

Component - Variation operators

part of parent 1 + part of parent 2 + mutation

8.7 4.7 9.5 5.9 6.2 1.3 1.8 … 1.0 8.7 3.8 1.7

8.5 9.1 0.1 3.5 8.0 1.5 2.2 … 1.6 5.5 2.8 4.5

8.7 4.7 3.4 5.9 6.2 1.7 2.2 … 1.6 5.5 2.8 4.5

Parent 1Parent 2

Offspring(+ mutation)

Component - survivor selection mechanism

Strengths• “Parachutist” comparison

– Parallelism:• multiple parachutists

– Mutation:• search in vicinity

– Selection:• focus on most successful

parachutists– Reproduction:

• give them walkie-talkies

Strengths

• Parallelism– CPU efficient– Problems with multiple objectives

• Blind watchmakers– Randomness = open mind

• Anytime behaviour

Strengths

• Wide range of applications

Limitations

• Defining the problem / strategy– Genotypes– Fitness function– Rate of mutation / crossover– Parental selection

• Premature convergence• Analytically solvable problems

Examples - Anaconda

• Kumar Chellapilla David B. Fogel

• Expert level

Examples - Anaconda

• your checker +1, enemy checker –1, no checker 0

• doesn’t know winning condition

• 15 neural networks that competed against each other

Examples - Faceprint

• Make composition drawings of criminals

• Hard to describe individual facial features

Examples - Faceprint

• 5 numbers for facial features

• 5 proportion

• Fitness is how close the picture is to the criminal

Examples – Movie Recommenders

• Film database– Genres

• How much will you like a filmif it is from the X, Y and Z genres?

• Neural network– Trained with Evolutionary Algorithm

Examples – Movie Recommenders

Score(will you like it?)

Genre (0 or 1):

Action

Adventure

Animation

Biography

…

Western

Examples – Movie Recommenders

• Phenotypes: 240 weights

• Genotypes: 8-bit floats

• DNA = long binary string– E.g. 110010010010100011 … 00100001010100

• Population of 200 DNA-strings– Evolved by crossover, mutation & selection

Examples – Movie Recommenders

• 11% better predictor than before

Thanks

Are there any questions?