Some guidelines for genetic algorithms with penalty functions
Proceedings of the third international conference on Genetic algorithms
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
An Adaptive Clustering Method Using a Geometric Shape for Vehicle Routing Problems with Time Windows
Proceedings of the 6th International Conference on Genetic Algorithms
GVR: A New Genetic Representation for the Vehicle Routing Problem
AICS '02 Proceedings of the 13th Irish International Conference on Artificial Intelligence and Cognitive Science
Vehicle Routing Problem: Doing It The Evolutionary Way
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
On the influence of GVR in vehicle routing
Proceedings of the 2003 ACM symposium on Applied computing
Parallel simulated annealing for the vehicle routing problem with time windows
EUROMICRO-PDP'02 Proceedings of the 10th Euromicro conference on Parallel, distributed and network-based processing
Multi-objective UAV mission planning using evolutionary computation
Proceedings of the 40th Conference on Winter Simulation
Route optimisation using evolutionary approaches for on-demand pickup problem
International Journal of Advanced Intelligence Paradigms
Optimization of Transport Plan for On-Demand Bus System Using Electrical Vehicles
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part II
Evolutionary trajectory planner for multiple UAVs in realistic scenarios
IEEE Transactions on Robotics
Simulation evaluation for on-demand bus system with electrical vehicles
Intelligent Decision Technologies - Special issue on design of intelligent environment
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Genetic Algorithms (GAs) can efficiently produce high quality results for hard combinatorial real world problems such as the Vehicle Routing Problem (VRP). Genetic Vehicle Representation (GVR), a recent approach to solving instances of the VRP with a GA, produces competitive or superior results to the standard benchmark problems. This work extends GVR research by presenting a more precise mathematical model of GVR than in previous works and a thorough comparison of GVR to Path Based Representation approaches. A suite of metrics that measures GVR's efficiency and effectiveness provides an adequate characterization of the jagged search landscape. A new variation of a crossover operator is introduced. A previously unmentioned insight about the convergence rate of the search is also noted that is especially important to the application of a priori and dynamic routing for swarms of Unmanned Aerial Vehicles (UAVs). Results indicate that the search is robust, and it exponentially drives toward high quality solutions in relatively short time. Consequently, a GA with GVR encoding is capable of providing a state-of-the-art engine for a UAV routing system or related application.