Genetic Algorithms Solve Combinatorial Optimisation Problems in the Calibration of Combustion Engines

Several combinatorial optimisation problems occur during the calibration of combustion engines. In this work, it is shown that three particular process steps benefit from genetic algorithms: First, the D-optimal experimental design is improved by the use of an appropriate crossover operator. Thereby the heuristics DETMAX or k-exchange perform a local search. The second problem concerns the optimal test bed scheduling for a more efficient and thus less expensive execution of measurements. This higher dimensional variant of the Travelling Salesman Problem (TSP) is solved by a hybrid genetic algorithm using adjacency coded individuals and a 2-opt heuristic as a local search. Finally, well-defined look-up tables, that lead to smooth maps, are composed from multiple valued look-up tables. Again a genetic algorithm finds better solutions than local search heuristics.

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