Genetic search on highly symmetric solution spaces: preliminary results
Theoretical aspects of evolutionary computing
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
The Effect of Spin-Flip Symmetry on the Performance of the Simple GA
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
A review of conflict detection and resolution modeling methods
IEEE Transactions on Intelligent Transportation Systems
ATOMS: Air Traffic Operations and Management Simulator
IEEE Transactions on Intelligent Transportation Systems
Application notes: MEBRA: multiobjective evolutionary-based risk assessment
IEEE Computational Intelligence Magazine
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Evaluating conflict resolution algorithms in the air-traffic domain is a challenging task. These algorithms are usually tested using a pair of aircraft or a limited number of geometries involving multiple aircraft. Our previous work demonstrated the use of evolutionary computation for risk assessment of air-traffic conflict detection algorithms using a red-teaming (or playing the devil) approach. This paper extends our previous work to conflict resolution and investigate the effect of symmetry in the representation on the performance of the evolutionary operators. Scenarios for testing air traffic conflict detection and resolution algorithms are each represented by a chromosome, which itself represents a group of pairs of aircraft in conflict. Each paired-conflict comes with its own set of parameters. However, any shuffling of the pairs does not change the definition of a scenario. If we have N pairs, any of the N! shuffles maps to the same phenotype. Therefore, there is high level symmetry in this problem. Because of the finite population size used in an evolutionary algorithm, one may expect that by fixing the position of each pair on the chromosome, a crossover operator that relies on the position of each gene is probably going to be inferior to one that does not. In this paper, we demonstrate, using a genetic algorithm, that -- despite the high level symmetry in this problem -- a position-dependent crossover is better than a position-independent crossover. This counterintuitive result identifies a potential efficient parameter setup for our future experiments in this problem domain.