Abstract
This paper presents a new hybrid approach ( $$\mathcal {N}$$ SGGA) combining steady-state grouping genetic algorithm with a local search for the maximally diverse grouping problem (MDGP) related to equal group-size. The MDGP is a well-known $$\mathcal {NP}$$ -Hard combinatorial optimization problem and finds numerous applications in real world. $$\mathcal {N}$$ SGGA employs particularly (a) crossover operator (b) the effective way of utilization of local search and (c) the additional replacement strategy, making it different from the existing genetic algorithm for the MDGP. On a set of large benchmark instances, $$\mathcal {N}$$ SGGA is competitive in comparison to the existing best-known approaches in the literature and performs particularly well on large-size instances. Some important ingredients of $$\mathcal {N}$$ SGGA that shed some light on the adequacy of $$\mathcal {N}$$ SGGA are analyzed.
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