Minimum attribute reduction (MAR) problem in the context of rough set theory is an NP-hard nonlinearly constrained combinatorial (binary) optimization problem.In this paper

a new combinatorial artificial bee colony (ABC) algorithm is presented for solving the MAR problem.Mutation operation based search schemes are introduced for employed bees

onlooker bees and scout bees.Two different metrics related to attribute subsets are used to generate candidate neighboring food sources.Different local search strategies between an employed bee and its recruited onlooker bees allow for a more diversified neighboring search around a current food source.Moreover

the information of the so-far best solution is exploited in various w

ays by employed bees

onlookers and scouts

respectively.Performance comparisons with existing best performing meta-heuristic approaches for the MAR problem were carried out on a number of UCI data sets.In addition

a standard statistical

t

-test is used for evaluation purpose.The experimental results show that our combinatorial ABC approach compares favorably with all the other approaches in terms of solution quality.The proposed combinatorial ABC algorithm is thus efficient and well suited for solving the MAR problem.