Distributed Modified Extremal Optimization for Reducing Crossovers in Reconciliation Graph

To determine the mechanism of molecular evolution, a reconciliation graph is constructed from two heterogeneous trees, which are referred to as ordered trees. In the reconciliation graph, the leaf nodes of the two ordered trees face each other. Furthermore, leaf nodes with the same label name are connected to each other by an edge. To carry out reconciliation work efficiently, it is necessary to find the state with the minimum number of crossovers of edges between leaf nodes. Reducing crossovers in a reconciliation graph is a combinatorial optimization problem. In this paper, we propose a novel bio-inspired algorithm called distributed modified extremal optimization (DMEO). This algorithm is a hybrid of population-based modified extremal optimization (PMEO) and the distributed genetic algorithm model that is used for reducing crossovers in a reconciliation graph. We have evaluated DMEO using actual data sets. DMEO shows better performance compared with PMEO.