introduction to the genetic algorithm

Introduction to the Genetic Algorithm

Qiang HaoLearning, Design and Technology & Computer Science

University of Georgia

DefinitionThe genetic algorithm (GA) is a search heuristic that mimics the process of natural selection.


● Purpose: To generate useful solutions to optimization and search problems.

● Reasons: Searching space is gigantically huge.


● Natural Selection:


● Natural Selection:a. Have an initial populationb. Selectionc. Crossover and mutation



Original: A, T, C, G, UAfterwards:

A, A, C, G, U


● Natural Selection:a. Have an initial populationb. Selectionc. Crossover and mutation Loop



Loop


● GA:a. Have an initial populationb. Selectionc. Crossover and mutation


● GA:a. Have an initial populationb. Selectionc. Crossover and mutationd. Termination



1. A genetic representation of the solution domain

2. A fitness function to evaluate the solution domain


● GA:1. A genetic representation of the solution domain2. A fitness function to evaluate the solution domain3. Have an initial population4. Selection5. Crossover and mutation6. Termination

Loop

ExampleMultiple fault diagnosis

http://bit.ly/1SVHsNJ

Potter, W. D., Miller, J. A., Tonn, B. E., Gandham, R. V., & Lapena, C. N. (1992). Improving the reliability of heuristic multiple fault diagnosis via the EC-based genetic algorithm. Applied Intelligence, 2(1), 5-23.




Given a set of manifestations, can you automatically determine what diseases cause this set of manifestations with acceptable accuracy?

1. We limit the total diagnosable manifestations to 10.2. These 10 manifestations are associated with 15 diseases.



● Step 1 - Bit representation: ○ {1, 0, 1, 0, 1, 1, 1, 1, 0, 1} -- manifestation○ {1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0} -- disease combination



● Step 2 - Fitness Function: ○ {1, 0, 1, 0, 1, 1, 1, 1, 0, 1} -- manifestation○ {1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0} -- disease combination○ {1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0} -- disease combination○ {1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0} -- disease combination



● Step 2 - Fitness Function:



● Step 3 - Have an initial population

600 random disease combinations

○ {1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0} -- disease combination○ {1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0} -- disease combination○ {1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0} -- disease combination



Step 4 - Selection

● Tournament Selection ● Roulette wheel selection



Step 4 - Selection

● Tournament Selection a. choose k individuals from the population at randomb. choose the best individual from pool

Population size: 600; tournament size: 6; repetition times: 600



Step 4 - Selection

● Roulette wheel selection

ExampleStep 4 - Selection

● Roulette wheel selection



Step 5 - Crossover

● One-point xover:● Two-point xover:



3. Have an initial population4. Selection 5. Crossover and mutation6. Termination

Loop

Example 2Forest Planning Optimization

http://bit.ly/1OmAxxn

Potter, W. D., Drucker, E., Bettinger, P., Maier, F., Martin, M., Luper, D., ... & Hayes, C. (2009). Diagnosis, configuration, planning, and pathfinding: Experiments in nature-inspired optimization. In Natural Intelligence for Scheduling, Planning and Packing Problems (pp. 267-294). Springer Berlin Heidelberg.




Given a forest composed of 73 adjacent fields, what cutting schedule would make the seasonal wood production closest to a fixed certain number?

1. Three cutting seasons per year2. Two adjacent fields can not both be cutted in one season




1. A genetic representation of the solution domain

2. A fitness function to evaluate the solution domain



● A genetic representation of the solution domain:[0, 2, 1, 3, 1 …...1, 0, 0, 1, 3, 2]



● A genetic representation of the solution domain:[0, 2, 1, 3, 1 …...1, 0, 0, 1, 3, 2]

● Fitness Function:(Output of season 1 - target)2 + (Output of season 2 - target)2 + (Output of season 3 - target)2

Thanks.

introduction to the genetic algorithm

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