  Feature selection has always been a critical step in pattern recognition, in
which evolutionary algorithms, such as the genetic algorithm (GA), are most
commonly used. However, the individual encoding scheme used in various GAs
would either pose a bias on the solution or require a pre-specified number of
features, and hence may lead to less accurate results. In this paper, a tribe
competition-based genetic algorithm (TCbGA) is proposed for feature selection
in pattern classification. The population of individuals is divided into
multiple tribes, and the initialization and evolutionary operations are
modified to ensure that the number of selected features in each tribe follows a
Gaussian distribution. Thus each tribe focuses on exploring a specific part of
the solution space. Meanwhile, tribe competition is introduced to the evolution
process, which allows the winning tribes, which produce better individuals, to
enlarge their sizes, i.e. having more individuals to search their parts of the
solution space. This algorithm, therefore, avoids the bias on solutions and
requirement of a pre-specified number of features. We have evaluated our
algorithm against several state-of-the-art feature selection approaches on 20
benchmark datasets. Our results suggest that the proposed TCbGA algorithm can
identify the optimal feature subset more effectively and produce more accurate
pattern classification.
