Usually, evolutionary computation (EC) is used for optimisation and machine learning tasks. Recently, a novel use of EC has been proposed - multiobjective evolutionary based risk assessment (MEBRA). MEBRA characterises the problem space associated with good and inferior performance of computational algorithms. Problem instances are represented ('scenario representation') and evolved ('scenario generation') in order to evaluate algorithms ('scenario evaluation'). The objective functions aim at maximising or minimising the success rate of an algorithm. In the 'scenano mining' step, MEBRA identifies the patterns common in problem instances when an algonthm performs best in order to understand when to use it, and in instances when it performs worst in ordei to understand when not to use it. So far, MEBRA has only been applied to a limited number of problems. Here the authors demonstrate its viability to efficiently detect hot spots in an algonthm's problem space. In particular, they apply the basic MEBRA rationale in the area of air traffic management (ATM). They examine two widely used algonthms for aircraft landing sequencing first come first served (FCFS) and constrained position shifting (CPS). Through the use of three different problem ('scenano') representations, they identify those patterns in ATM problems that signal instances when CPS performs better than FCFS, and those when it performs worse. They show that scenano representation affects the quality of MEBRA outputs. In particular, they find that the vanable-length chromosome representation of aircraft scheduling sequence scenanos converges fast and finds all relevant nsk patterns associated with the use of FCFS and CPS.
Evolutionary-computation based risk assessment of aircraft landing sequencing algorithms
Risikoanalyse auf Basis evolutionärer Algorithmen für die Lenkung von Flugzeuglandungen
2010
12 Seiten, 2 Bilder, 5 Tabellen, 10 Quellen
Conference paper
English
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