Selection of a representative set of features is still a crucial and challeng-
ing problem in machine learning. The complexity of the problem increases
when any of the following situations occur: a very large number of at-
tributes (large dimensionality); a very small number of instances or time
points (small-instance set). The rst situation poses problems for machine
learning algorithm as the search space for selecting a combination of relevant
features becomes impossible to explore in a reasonable time and with rea-
sonable computational resources. The second aspect poses the problem of
having insu cient data to learn from (insu cient examples). In this work,
we approach both these issues at the same time. The methods we proposed
are heuristics inspired from nature (in particular, from biology). We pro-
pose a hybrid of two methods which has the advantage of providing a good
learning from fewer examples and a fair selection of features from a really
large set, all these while ensuring a high standard classi cation accuracy of
the data. The methods used are antlion optimization (ALO), grey wolf opti-
mization (GWO), and a combination of the two (ALO-GWO). We test their
performance on datasets having almost 50,000 features and less than 200
instances. The results look promising while compared with other methods
such as genetic algorithms (GA) and particle swarm optimization (PSO)
