Abstract
In this paper an island model is described for the unconstrained Binary Quadratic Problem (BQP), which can be used with up to 2500 binary variables. Our island model uses a master-slave structure and the migration is centralized. In the model a basic evolutionary algorithm (EA) runs which is a hybrid, steady-state EA. The basic EA uses a new mutation operator that is composed of two parts and based on a modified version of an explicit collective memory method (EC-memory), the Virtual Loser [2].We tested our island model on the benchmark problems from the OR-Library. Comparing the results with other heuristic methods, we can conclude that our algorithm is highly effective in solving large instances of the BQP; it has a high probability of finding the best-known solutions.
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