Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Machine Learning
Handbook of Neural Computation
Handbook of Neural Computation
Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
Random Dynamics Optimum Tracking with Evolution Strategies
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Combining Evolutionary And Non-evolutionary Methods In Tracking Dynamic Global Optima
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
ISMIS '99 Proceedings of the 11th International Symposium on Foundations of Intelligent Systems
Tracking Extrema in Dynamic Environments
EP '97 Proceedings of the 6th International Conference on Evolutionary Programming VI
Temporal credit assignment in reinforcement learning
Temporal credit assignment in reinforcement learning
Scalability problems of simple genetic algorithms
Evolutionary Computation
The naive MIDEA: a baseline multi-objective EA
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Dynamic multi-objective optimization with evolutionary algorithms: a forward-looking approach
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Dynamic Time-Linkage Problems Revisited
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
Evolutionary and embryogenic approaches to autonomic systems
Proceedings of the 3rd International Conference on Performance Evaluation Methodologies and Tools
Improving prediction in evolutionary algorithms for dynamic environments
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
A survey of evolutionary and embryogenic approaches to autonomic networking
Computer Networks: The International Journal of Computer and Telecommunications Networking
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Using genetic algorithms for navigation planning in dynamic environments
Applied Computational Intelligence and Soft Computing
International Journal of Adaptive, Resilient and Autonomic Systems
An improved firefly algorithm for solving dynamic multidimensional knapsack problems
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
In this paper we focus on an important source of problem-difficulty in (online) dynamic optimization problems that has so far received significantly less attention than the traditional shifting of optima. Intuitively put, decisions taken now (i.e. setting the problem variables to certain values) may influence the score that can be obtained in the future. We indicate how such time-linkage can deceive an optimizer and cause it to find a suboptimal solution trajectory. We then propose a means to address time-linkage: predict the future by learning from the past. We formalize this means in an algorithmic framework. Also, we indicate why evolutionary algorithms are specifically of interest in this framework. We have performed experiments with two new benchmark problems that contain time-linkage. The results show, as a proof of principle, that in the presence of time-linkage EAs based upon this framework can obtain better results than classic EAs that do not predict the future.