Evolutionary Voting-Based Extreme Learning Machines

Algorithm 1

Evolutionary voting based ELM.

Inputs: Training samples with labels ; : Population

of candidates at generation ; : Population size of each generation; : Crossover probability;

: Mutation probability

Initialization: Set and initialize the population at random.

Evolutionary Process: Evaluate the fitness values of using (5). Increase with 1 in each

iteration. To create a new generation , operations of selection, crossover and mutation

on are used. Repeat the following steps until the termination criteria of genetic algorithm

(GA) is met.

(i) Firstly, members of are probabilistically selected to according to the

fitness.

(ii) Secondly, the crossover operator is applied to half of not selected candidates in . The

offsprings after crossover are added to .

(iii) Lastly, a number of chromosomes with a probability of in are subject to mutation.

Store weights as the outputs where “opt” indicates “optimal”.

Decision Making: Given a testing sample , use the following equation to predict its label