Advances in Artificial Intelligence / 2014 / Article / Alg 2

Research Article

A New Evolutionary-Incremental Framework for Feature Selection

Algorithm 2

Evolutionary-incremental genetic algorithm.
Input: : recombination probability, : mutation probability, : addition probability, : deletion probability, : maximum
length of solutions, : number of generations that is constant during these generations
(i) Initialization: , is an auxiliary variable, is a random initial population with the maximum length of solutions
is equal to
(ii) Evaluation: calculation of fitness value of each solution in the population
(iii)
(iv) If then and
(v) Parent Selection: randomly selection of parents from the population by a given parent selection method (e.g., roulette wheel)
(vi) Recombination: applying a recombination operator (e.g., 1-point crossover) on each pair of parents with the probability
of to generate new offspring
(vii) Mutation: applying a mutation operator (e.g., uniform mutation) on each offspring generated from the previous step with
the probability of
(viii) Addition: applying addition operator on each offspring generated from the previous step with the probability of while
the length of solutions must be less than or equal to
(ix) Deletion: applying deletion operator on each offspring generated from the previous step with the probability of while
the length of solutions must be greater than or equal to 1
(x) Evaluation: calculation of fitness value of offspring
(xi) Survival Selection: randomly selection of the individuals from the current population and generated offspring by a given
survival selection method (e.g., fitness based or age based)
(xii) Termination Condition: if termination condition(s) is satisfied, the algorithm is finished; otherwise go to step (iii).
When the algorithm is finished, the best solution with the highest fitness value is the output of the algorithm.

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