By Shengxiang Yang, Xin Yao
This publication offers a compilation at the state of the art and up to date advances of evolutionary computation for dynamic optimization difficulties. the incentive for this booklet arises from the truth that many real-world optimization difficulties and engineering structures are topic to dynamic environments, the place adjustments happen through the years.
Key concerns for addressing dynamic optimization difficulties in evolutionary computation, together with basics, set of rules layout, theoretical research, and real-world functions, are provided. "Evolutionary Computation for Dynamic Optimization difficulties" is a important connection with scientists, researchers, pros and scholars within the box of engineering and technological know-how, relatively within the components of computational intelligence, nature- and bio-inspired computing, and evolutionary computation.
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Extra info for Evolutionary Computation for Dynamic Optimization Problems
More details on the DF1 generator can be found in . 2 The Moving Peaks Benchmark (MPB) Problem Branke  proposed the MPB problem, which has been widely used as dynamic benchmark problems in the literature. , the height, width, and central position. Within the MPB problem, the optima can be changed by changing the three features of each peak independently or in a correlative way. 0 where Wi (t) and Hi (t) are the height and width of peak i at time t, respectively, and Xi j (t) is the j-th element of the location of peak i at time t.
This XOR DOP generator has two properties. One is that the distances among the solutions in the search space remains unaltered after an environmental change. The other is that the properties of the fitness landscape are not changed after an environmental change, which facilitates the analysis of the behavior of algorithms. Recently, the XOR DOP generator has been extended to construct dynamic problems in the real space . In , two continuous dynamic problem generators were proposed using the linear transformation of individuals.
3 23 Convergence Speed after Changes Convergence speed after changes, or the ability of the algorithm to recover quickly after a change, is also an aspect that attracts the attention of various studies in EDO. In fact many of the optimality-based measures, such as the offline error/performance, best-of-generation, relative-ratio-of-best-value discussed previously can be used to indirectly evaluate the convergence speed. In addition, in , the author also proposed a measure dedicated to evaluating the ability of an adaptive algorithm to react quickly to changes.
Evolutionary Computation for Dynamic Optimization Problems by Shengxiang Yang, Xin Yao