An introduction to differential evolution
New ideas in optimization
A species conserving genetic algorithm for multimodal function optimization
Evolutionary Computation
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Division of labor in particle swarm optimisation
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Adaptive elitist-population based genetic algorithm for multimodal function optimization
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
A novel evolutionary drug scheduling model in cancer chemotherapy
IEEE Transactions on Information Technology in Biomedicine
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This paper introduces a novel genetic variable representation for dynamic optimization problems in evolutionary computation. This variable representation allows static evolutionary optimization approaches to be extended to efficiently explore global and better local optimal areas in dynamic fitness landscapes. It represents a single individual as a pair of real-valued vector (x, r) ∈ Rn x R2 in the evolutionary search population. The first vector x corresponds to a point in the n-dimensional search space (an object variable vector), while the second vector r represents the dynamic fitness value and the dynamic tendency of the individual x in the dynamic environment. r is the control variable (also called strategy variable), which allow self-adaptation. The object variable vector x is operated by different genetic strategies according to its corresponding r. As a case study, we have integrated the new variable representation into Genetic Algorithms (GAs), yielding an Dynamic Optimization Genetic Algorithm (DOGA). DOGA is experimentally tested with 5 benchmark dynamic problems. The results all demonstrate that DOGA consistently outperforms other GAs on dynamic optimization problems.