  Swarm intelligence is a discipline that studies the collective behavior that
is produced by local interactions of a group of individuals with each other and
with their environment. In Computer Science domain, numerous swarm intelligence
techniques are applied to optimization problems that seek to efficiently find
best solutions within a search space. Gradual pattern mining is another
Computer Science field that could benefit from the efficiency of swarm based
optimization techniques in the task of finding gradual patterns from a huge
search space. A gradual pattern is a rule-based correlation that describes the
gradual relationship among the attributes of a data set. For example, given
attributes {G,H} of a data set a gradual pattern may take the form: "the less
G, the more H". In this paper, we propose a numeric encoding for gradual
pattern candidates that we use to define an effective search space. In
addition, we present a systematic study of several meta-heuristic optimization
techniques as efficient solutions to the problem of finding gradual patterns
using our search space.
