  This paper proposes a novel Extended Particle Swarm Optimization model (EPSO)
that potentially enhances the search process of PSO for optimization problem.
Evidently, gene expression profiles are significantly important measurement
factor in molecular biology that is used in medical diagnosis of cancer types.
The challenge to certain classification methodologies for gene expression
profiles lies in the thousands of features recorded for each sample. A modified
Wrapper feature selection model is applied with the aim of addressing the gene
classification challenge by replacing its randomness approach with EPSO and PSO
respectively. EPSO is initializing the random size of the population and
dividing them into two groups in order to promote the exploration and reduce
the probability of falling in stagnation. Experimentally, EPSO has required
less processing time to select the optimal features (average of 62.14 sec) than
PSO (average of 95.72 sec). Furthermore, EPSO accuracy has provided better
classification results (start from 54% to 100%) than PSO (start from 52% to
96%).
