"This paper presents a novel evolutionary algorithm for solving routing and sequencing problems. It builds directly on the observation that the optimal solution is composed mostly of short and low-cost links. It uses an indirect representation and an extended nearest neighbor constructive procedure. The representation scheme is redundant making it possible to have multiple attractors in the search space representing the same optimal solution. The order-based crossover is used that allows for combining partial solutions contained in parental individuals. Further, an adaptive iterated scheme is used to allow for escaping from a region of attraction of a local optimum. Proof-of-concept experiments have been carried out on the classical TSP with data sets ranging from 100 to 2392 nodes. The proposed algorithm was compared with two other relevant EAs. The achieved results clearly show the proposed method is competitive to and better than the compared EAs." . . "A Novel Evolutionary Algorithm with Indirect Representation and Extended Nearest Neighbor Constructive Procedure for Solving Routing Problems"@en . "IEEE" . . . . . . "21230" . "A Novel Evolutionary Algorithm with Indirect Representation and Extended Nearest Neighbor Constructive Procedure for Solving Routing Problems" . . "A Novel Evolutionary Algorithm with Indirect Representation and Extended Nearest Neighbor Constructive Procedure for Solving Routing Problems" . "Okinawa" . . "RIV/68407700:21230/14:00222878!RIV15-MSM-21230___" . "Piscataway" . "2"^^ . "A Novel Evolutionary Algorithm with Indirect Representation and Extended Nearest Neighbor Constructive Procedure for Solving Routing Problems"@en . "2"^^ . "978-1-4799-7938-7" . . . "2014-11-27+01:00"^^ . "This paper presents a novel evolutionary algorithm for solving routing and sequencing problems. It builds directly on the observation that the optimal solution is composed mostly of short and low-cost links. It uses an indirect representation and an extended nearest neighbor constructive procedure. The representation scheme is redundant making it possible to have multiple attractors in the search space representing the same optimal solution. The order-based crossover is used that allows for combining partial solutions contained in parental individuals. Further, an adaptive iterated scheme is used to allow for escaping from a region of attraction of a local optimum. Proof-of-concept experiments have been carried out on the classical TSP with data sets ranging from 100 to 2392 nodes. The proposed algorithm was compared with two other relevant EAs. The achieved results clearly show the proposed method is competitive to and better than the compared EAs."@en . . "[D1EDFD6CC8D4]" . "Proccedings of 2014 International Conference on Intelligent Systems Design and Applications" . "RIV/68407700:21230/14:00222878" . . "S, Z(MSM6840770038)" . . "993" . "Sn\u00ED\u017Eek, Michal" . . . "Kubal\u00EDk, Ji\u0159\u00ED" . . "Combinatorial Optimization; Evolutionary Algorithms; Representation"@en . "6"^^ . . .