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Keynote Talk at BENELEARN 2016

In September 2016 I gave a keynote talk at BENELEARN 2016 which took place in Kortrijk, Belgium.

Titel: Combining metaheuristics with ILP solvers: Construct, Merge, Solve & Adapt

Abstract: Metaheuristics such as evolutionary algorithms and tabu search are approximate methods for optimization. The combination of metaheuristics with complete techniques, such as integer linear programming (ILP) solvers, is one of the current lines of research in combinatorial optimization. The main aim behind such approaches is to exploit the complementary character of different optimization strategies in order to obtain robust algorithms that generate high-quality solutions in reasonable computation times. Given a combinatorial optimization problem, general purpose ILP solvers such as CPLEX are often highly efficient for solving small to medium sized problem instances. This is because they are the result of many years of research. Moreover, they make use of efficient implementations of cutting edge technologies in ILP solving. However, naturally they reach their limits with growing problem instance size. One of the motivations for the combination of metaheuristics with ILP solvers such as CPLEX is the aim of taking profit from the application of ILP solvers even when the considered problem instances are too large for applying these solvers directly. In this keynote talk we will present our most recent algorithmic development that resulted from the motivation outlined above: construct, merge, solve & adapt (CMSA). Moreover, we explore the relation of CMSA with large neighborhood search (LNS), which is one of the standard ways of combining local search with ILP solvers.

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