Nonstationary function optimization using genetic algorithm with dominance and diploidy
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
Case-Based Initialization of Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
Genetic Algorithms for Tracking Changing Environments
Proceedings of the 5th International Conference on Genetic Algorithms
A New Diploid Scheme and Dominance Change Mechanism for Non-Stationary Function Optimization
Proceedings of the 6th International Conference on Genetic Algorithms
A Comparison of Dominance Mechanisms and Simple Mutation on Non-stationary Problems
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
A Comparative Study of Steady State and Generational Genetic Algorithms
Selected Papers from AISB Workshop on Evolutionary Computing
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Evolutionary algorithms for dynamic optimization problems: workshop preface
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
A comparative study of immune system based genetic algorithms in dynamic environments
Proceedings of the 8th annual conference on Genetic and evolutionary computation
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
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
Impact of Frequency and Severity on Non-Stationary Optimization Problems
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
Evolutionary and embryogenic approaches to autonomic systems
Proceedings of the 3rd International Conference on Performance Evaluation Methodologies and Tools
Dynamic evolutionary optimisation: an analysis of frequency and magnitude of change
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Variable-Size Memory Evolutionary Algorithm to Deal with Dynamic Environments
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
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
Triggered Memory-Based Swarm Optimization in Dynamic Environments
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
Novel Associative Memory Retrieving Strategies for Evolutionary Algorithms in Dynamic Environments
ISICA '09 Proceedings of the 4th International Symposium on Advances in Computation and Intelligence
A survey of evolutionary and embryogenic approaches to autonomic networking
Computer Networks: The International Journal of Computer and Telecommunications Networking
Multimodal dynamic optimization: from evolutionary algorithms to artificial immune systems
ICARIS'07 Proceedings of the 6th international conference on Artificial immune systems
An adaptive optimization technique for dynamic environments
Engineering Applications of Artificial Intelligence
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Ant colony optimization with immigrants schemes in dynamic environments
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
Memory-based CHC algorithms for the dynamic traveling salesman problem
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Memory-based immigrants for ant colony optimization in changing environments
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Fuzzy genetic sharing for dynamic optimization
International Journal of Automation and Computing
Differential evolution for dynamic environments with unknown numbers of optima
Journal of Global Optimization
An improved firefly algorithm for solving dynamic multidimensional knapsack problems
Expert Systems with Applications: An International Journal
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Investigating and enhancing the performance of genetic algorithms in dynamic environments have attracted a growing interest from the community of genetic algorithms in recent years. This trend reflects the fact that many real world problems are actually dynamic, which poses serious challenge to traditional genetic algorithms. Several approaches have been developed into genetic algorithms for dynamic optimization problems. Among these approches, random immigrants and memory schemes have shown to be beneficial in many dynamic problems. This paper proposes a hybrid memory and random immigrants scheme for genetic algorithms in dynamic environments. In the hybrid scheme, the best solution in memory is retrieved and acts as the base to create random immigrants to replace the worst individuals in the population. In this way, not only can diversity be maintained but it is done more efficiently to adapt the genetic algorithm to the changing environment. The experimental results based on a series of systematically constructed dynamic problems show that the proposed memory-based immigrants scheme efficiently improves the performance of genetic algorithms in dynamic environments.