Optimize Selection (Evolutionary)


Synopsis

A genetic algorithm for feature selection.


Description

A genetic algorithm for feature selection (mutation=switch features on and off, crossover=interchange used features). Selection is done by roulette wheel. Genetic algorithms are general purpose optimization / search algorithms that are suitable in case of no or little problem knowledge.

A genetic algorithm works as follows
    * Generate an initial population consisting of population_size individuals. Each attribute is switched on with probability p_initialize
    • For all individuals in the population
    • Perform mutation, i.e. set used attributes to unused with probability p_mutation and vice versa.
    • Choose two individuals from the population and perform crossover with probability p_crossover. The type of crossover can be selected by crossover_type.
    • Perform selection, map all individuals to sections on a roulette wheel whose size is proportional to the individual's fitness and draw population_size individuals at random according to their probability.
    • As long as the fitness improves, go to 2
If the example set contains value series attributes with blocknumbers, the whole block will be switched on and off.

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ExampleProcess