Multidimensional binary search trees used for associative searching
Communications of the ACM
Case-Based Reasoning: Experiences, Lessons and Future Directions
Case-Based Reasoning: Experiences, Lessons and Future Directions
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Investigation of Different Seeding Strategies in a Genetic Planner
Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
Multi-objective Improvement of Software Using Co-evolution and Smart Seeding
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
Intelligent bias of network structures in the hierarchical BOA
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Automatic algorithm configuration based on local search
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II
IEEE Transactions on Evolutionary Computation
The performance of a new version of MOEA/D on CEC09 unconstrained MOP test instances
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Multiobjective A* search with consistent heuristics
Journal of the ACM (JACM)
Multiple objective optimisation applied to route planning
Proceedings of the 13th annual conference on Genetic and evolutionary computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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Planning a satisfactory route for an autonomous vehicle over a complex unstructured environment with respect to multiple objectives is a time consuming task. However, there are abundant opportunities to speed up the process by exploiting prior information, either from approximations of the current problem instance or from previously solved instances of similar problems. We examine these two approaches for a set of test instances in which the competing objectives are the time taken and likelihood of detection, using real-world data sources (Digital Terrain Elevation Data and Hyperspectral data) to estimate the objectives. Five different instances of the problem are used, and initially we compare three multi-objective optimisation evolutionary algorithms (MOEA) on these instances, without involving prior information. Using the best-performing MOEA, we then evaluate two approaches that exploit prior information; a graph-based approximation method that pre-computes a collection of generic 'coarse-grained' routes between randomly selected zones in the terrain, and a memory-based approach that uses the solutions to previous instances. In both cases the prior information is queried to find previously solved instances (or pseudo-instances, in the graph based approach) that are similar to the instance in hand, and these are then used to seed the optimisation. We find that the memory based approach is most effective, however this is only usable when prior instances are available.