Discrete Bacterial Memetic Evolutionary Algorithms for Solving High Complexity Problems: PLENARY TALK

Abstract

In the talk, several examples will be presented with standard benchmarks going up to large numbers of graph nodes, and the DBMEA results will be compared with the best practices from the literature. The predictability feature will also be illustrated by size-running time graphs. Reference will be made to the importance of determining the initial population in achieving fast and accurate results. A new approach, the Bounded Radius Heuristics will be presented. In the last part of the talk, a series of fuzzy extensions of the Time Dependent TSP (TD TSP) will be introduced, an extension of the TSP with real life aspects where the natural fluctuation of the traffic in certain areas causes non-deterministic features causing additional difficulties in the quasi-optimization. The novel extensions will be also tackled with the DBMEA approach successfully. As a conclusion, one more example will be mentioned where the discrete NP-hard problem is of a rther different nature, and it will be shown that by changing the local search technique appropriately, DBMEA can still deliver superior results.