Introduction to Genetic AlgorithmPrinciple: survival-of-the-fitness Characteristics of GARobustError-tolerantFlexibleWhen you have no idea about solving problems

Component of Genetic AlgorithmRepresentationGenetic operations:Crossover, mutation,inversion, as you wishSelectionElitism, total, steady state,as you wishFitnessProblem dependent

Everybody has different survival approaches.

How to implement a GA ?RepresentationFitness Operators designSelection strategy

Example(I)Maximize

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Sheet2

Sheet3

Example(I): RepresentationStandard GA binary stringx = 5, x = 101x = 3.25 x = 011.1Something noticeableLength is predefined.Not the only way.chromosomegene

Example(I): Fitness functionIn this case, it is known already

Example(I): Genetic OperatorStandard crossover (one-point crossover)

Example(I): Genetic OperatorStandard mutation (point mutation)

Example(I): SelectionStandard selection (roulette wheel)

Chart1

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Sheet2

Sheet3

Example(II)Minimize

Example(II): RepresentationStandard GA binary stringToo complexIntuitivelyReal numbered coding

Example(II): Fitness functionIn this case, it is known already

Example(II): Genetic OperatorStandard crossover (multi-point crossover)

Example(II): Genetic OperatorStandard mutation (multi-point mutation)

0.3, 0.7, 1.2, 3.5, -9.87, 2.334, 34 0.3, 0.7, 0.9, 3.5, -9.87, 2.557, 34

Example(II): SelectionStandard selection (rank selection)

Comparing Two Selection