Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
A Population Based Approach for ACO
Proceedings of the Applications of Evolutionary Computing on EvoWorkshops 2002: EvoCOP, EvoIASP, EvoSTIM/EvoPLAN
A Fast Evolutionary Algorithm for Traveling Salesman Problem
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 04
Genetic algorithms with memory-and elitism-based immigrants in dynamic environments
Evolutionary Computation
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
Hyper-learning for population-based incremental learning in dynamic environments
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A memory enhanced evolutionary algorithm for dynamic scheduling problems
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
An Agent-based Evolutionary Search for Dynamic Travelling Salesman Problem
ICIE '10 Proceedings of the 2010 WASE International Conference on Information Engineering - Volume 01
CHC-based algorithms for the dynamic traveling salesman problem
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
A new approach to solving dynamic traveling salesman problems
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
Population-Based Incremental Learning With Associative Memory for Dynamic Environments
IEEE Transactions on Evolutionary Computation
Virtual loser genetic algorithm for dynamic environments
EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation
Parallel CHC algorithm for solving dynamic traveling salesman problem using many-core GPU
AIMSA'12 Proceedings of the 15th international conference on Artificial Intelligence: methodology, systems, and applications
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The use of memory-based Evolutionary Algorithms (EAs) for dynamic optimization problems (DOPs) has proved to be efficient, namely when past environments reappear later. Memory EAs using associative approaches store the best solution and additional information about the environment. In this paper we propose a new algorithm called Extended Virtual Loser Genetic Algorithm (eVLGA) to deal with the Dynamic Traveling Salesman Problem (DTSP). In this algorithm, a matrix called extended Virtual Loser (eVL) is created and updated during the evolutionary process. This matrix contains information that reflects how much the worst individuals differ from the best, working as environmental information, which can be used to avoid past errors when new individuals are created. The matrix is stored into memory along with the current best individual of the population and, when a change is detected, this information is retrieved from memory and used to create new individuals that replace the worst of the population. eVL is also used to create immigrants that are tested in eVLGA and in other standard algorithms. The performance of the investigated eVLGAs is tested in different instances of the Dynamic Traveling Salesman Problem and compared with different types of EAs. The statistical results based on the experiments show the efficiency, robustness and adaptability of the different versions of eVLGA.