Evaluating evolutionary algorithms
Artificial Intelligence - Special volume on empirical methods
A Population Based Approach for ACO
Proceedings of the Applications of Evolutionary Computing on EvoWorkshops 2002: EvoCOP, EvoIASP, EvoSTIM/EvoPLAN
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
Population-based incremental learning with memory scheme for changing environments
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Memory-based immigrants for genetic algorithms in dynamic environments
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
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
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Genetic Algorithms with Elitism-Based Immigrants for Changing Optimization Problems
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: 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
The memory indexing evolutionary algorithm for dynamic environments
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
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The CHC algorithm uses an elitist selection method that, combined with an incest prevention mechanism and a method to diverge the population whenever it converges, allows the maintenance of the population diversity. This algorithm was successfully used in the past for static optimization problems. The use of memory in Evolutionary Algorithms has been proved to be advantageous when dealing with dynamic optimization problems. In this paper we investigate the use of three different explicit memory strategies included in the CHC algorithm. These strategies - direct, immigrant and associative - combined with the CHC algorithm are used to solve different instances of the dynamic Traveling Salesman Problem in cyclic, noisy and random environments. The experimental results, statistically validated, show that the memory schemes significantly improve the performance of the original CHC algorithm for all types of studied environments. Moreover, when compared with the equivalent memory-based standard EAs with the same memory schemes, the memory-based CHC algorithms obtain superior results when the environmental changes are slower.