Gene Selection via a New Hybrid Ant Colony Optimization Algorithm for Cancer Classification in High-Dimensional Data

Algorithm 4

Proposed approach (MWIS-ACO-LS).

	Input: DNA microarray data;
	Output The global best candidate solution .
	Begin
	Stage 1: The selection of the first subset of gene
3:	Step 1: Use the Algorithm 1 to construct the gene-similarity graph.
	Step 2: Apply the greedy algorithm (Algorithm 1) to select an initial subset of genes.
	Stage 2: The application of ACO to the subset of gene selected in the first stage
6:	Step 1: ACO combined with the local search
	Initialize the pheromone matrix by ones.
	for do
9:	for do
	build the path (candidate solution S)of the ant based on the probabilistic decision rule defined by (4), (5) and (6).
	Calculate the fitness of the candidate solution using LOOCV in (11).
12:	if i = = 1 then
	end if
15:	if then
	end if
18:	Do a local update of pheromones based on S.
	end for
	Apply the Local search (Algorithm 3) to .
21:	Do a global update of pheromones based on .
	end for
	Find the global best solution
24:	Step 2: Apply a backward generation to .
	Return .