Titlebook: Evolutionary Computation for Dynamic Optimization Problems; Shengxiang Yang,Xin Yao Conference proceedings 2013 Springer-Verlag Berlin Hei

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Anmerkungen, Rechtsprechung und Literatur,s been widely applied to solve dynamic optimization problems (DOPs) with variant enhancements, e.g., diversity maintaining schemes, memory schemes, multipopulation schemes, adaptive schemes, and hybrid schemes. In this chapter, we categorize and review approaches proposed based on PSO for DOPs. Weak

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Methodologie und Forschungsprozess,l changes. This chapter investigates the application of memetic algorithms, a class of hybrid evolutionary algorithms, for dynamic optimization problems. An adaptive hill climbing method is proposed as the local search technique in the framework of memetic algorithms, which combines the features of

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Ethiopian Yearbook of International Lawk of dynamic fitness landscapes. In this chapter, we define such dynamic fitness landscapes and discuss their properties. To this end, analyzing tools for measuring topological and dynamical landscape properties are studied. Based on these landscape measures we obtain an approach for drawing conclus

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https://doi.org/10.1007/978-3-658-37766-32) they have multiple objectives. However, current research in the field of dynamic multi-objective optimization (DMO) is relatively sparse. In this chapter, we review recent work in this field and present our analysis of the subset sum problem in the DMO variant. Our approach uses a genetic algorit