Online computation and competitive analysis
Online computation and competitive analysis
Better Bounds for Online Scheduling
SIAM Journal on Computing
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Developments from a June 1996 seminar on Online algorithms: the state of the art
Drive: Dynamic Routing of Independent Vehicles
Operations Research
Learning, anticipation and time-deception in evolutionary online dynamic optimization
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Regrets only! online stochastic optimization under time constraints
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Online stochastic and robust optimization
ASIAN'04 Proceedings of the 9th Asian Computing Science conference on Advances in Computer Science: dedicated to Jean-Louis Lassez on the Occasion of His 5th Cycle Birthday
Attributes of Dynamic Combinatorial Optimisation
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
ISICA '08 Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence
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
Evolutionary Multiobjective Optimization for Dynamic Hospital Resource Management
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
Memory based on abstraction for dynamic fitness functions
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
Distributed genetic evolution in WSN
Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks
Proceedings of the 12th annual conference on Genetic and evolutionary computation
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
Policy learning in resource-constrained optimization
Proceedings of the 13th annual conference on Genetic and evolutionary computation
On handling ephemeral resource constraints in evolutionary search
Evolutionary Computation
An improved firefly algorithm for solving dynamic multidimensional knapsack problems
Expert Systems with Applications: An International Journal
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The focus of this paper is on how to design evolutionaryalgorithms (EAs) for solving stochastic dynamicoptimization problems online, i.e.~as time goes by.For a proper design, the EA must not only be capableof tracking shifting optima, it must also take intoaccount the future consequences of the evolveddecisions or actions. A previousframework describes how to build such EAs in thecase of non-stochastic problems. Most real-worldproblems however are stochastic. In this paper weshow how this framework can be extended to properlytackle stochasticity. We point out how thisnaturally leads to evolving strategiesrather than explicit decisions. We formalizeour approach in a new framework. The newframework and the various sourcesof problem-difficulty at hand are illustratedwith a running example. We also apply ourframework to inventory management problems, an importantreal-world application area in logistics. Our results show,as a proof of principle, the feasibility and benefitsof our novel approach.