  It has been widely recognized that the performance of a multi-agent system is
highly affected by its organization. A large scale system may have billions of
possible ways of organization, which makes it impractical to find an optimal
choice of organization using exhaustive search methods. In this paper, we
propose a genetic algorithm aided optimization scheme for designing
hierarchical structures of multi-agent systems. We introduce a novel algorithm,
called the hierarchical genetic algorithm, in which hierarchical crossover with
a repair strategy and mutation of small perturbation are used. The phenotypic
hierarchical structure space is translated to the genome-like array
representation space, which makes the algorithm genetic-operator-literate. A
case study with 10 scenarios of a hierarchical information retrieval model is
provided. Our experiments have shown that competitive baseline structures which
lead to the optimal organization in terms of utility can be found by the
proposed algorithm during the evolutionary search. Compared with the
traditional genetic operators, the newly introduced operators produced better
organizations of higher utility more consistently in a variety of test cases.
The proposed algorithm extends of the search processes of the state-of-the-art
multi-agent organization design methodologies, and is more computationally
efficient in a large search space.
