Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
Adaption to a Changing Environment by Means of the Thermodynamical Genetic Algorithm
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Designing evolutionary algorithms for dynamic environments
Designing evolutionary algorithms for dynamic environments
Case Study: An Intelligent Decision-Support System
IEEE Intelligent Systems
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Dynamic optimization by evolutionary algorithms applied to financial time series
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
A self-organizing random immigrants genetic algorithm for dynamic optimization problems
Genetic Programming and Evolvable Machines
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
Dynamic multiobjective optimization problems: test cases, approximation, and applications
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Analysis of the (1+1) EA for a dynamically bitwise changing ONEMAX
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Vector prediction approach to handle dynamical optimization problems
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
Dynamic multiobjective optimization problems: test cases, approximations, and applications
IEEE Transactions on Evolutionary Computation
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
A classification of dynamic multi-objective optimization problems
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
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Dynamic Optimization Evolutionary Algorithm (DOEA) is an intrinsic development of traditional Evolutionary Algorithm. Different to the traditional Evolutionary Algorithm which is designed for stationary or static optimization functions, it can be used to solve some dynamic optimization problems. The traditional Evolutionary Algorithm is hard to escape from the old optimum after the convergence when dealing with dynamic optimization problems, therefore, it is necessary to develop new algorithms. After reviewing the relative works, three directions are proposed: first, by treating the time variable as a common variable, DOPs can be extended as a kind of special Multi-objective Optimization Problems, therefore, Multi-objective Optimization Evolutionary Algorithm would be useful to develop DOEAs; second, it would be very important to theoretically analyze Dynamic Optimization Evolutionary Algorithm; finally, DOEA can be applied into more fields, such as industrial control etc..