Advances in Artificial Intelligence / 2014 / Article / Alg 1

Research Article

A New Evolutionary-Incremental Framework for Feature Selection

Algorithm 1

Standard genetic algorithm.
Input: : recombination probability, : mutation probability
(i) Initialization: is a random initial population
(ii) Evaluation: calculation of fitness value of each solution in the population
(iii) Parent Selection: randomly selection of parents from the population by a given parent selection method (e.g., roulette wheel)
(iv) Recombination: applying a recombination operator (e.g., 1-point crossover) on each pair of parents with the probability
    of to generate new offspring
(v) Mutation: applying a mutation operator (e.g., uniform mutation) on each offspring generated from the previous step
   with the probability of
(vi) Evaluation: calculation of fitness value of offspring
(vii) Survival Selection: randomly selection of individuals from the current population and generated offspring by a given
  survival selection method (e.g., fitness based or age based)
(viii) 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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