Summary
In this chapter, we discuss the use of multiobjective evolutionary algorithms (MOEAs) for solving single-objective optimization problems in dynamic environments. Specifically, we investigate the consideration of a second (artificial) objective, with the aim of maintaining greater population diversity and adaptability. The paper suggests and compares a number of alternative ways to express this second objective. An empirical comparison shows that the best alternatives are competitive with other evolutionary algorithm variants designed for handling dynamic environments.
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Bui, L.T., Nguyen, MH., Branke, J., Abbass, H.A. (2008). Tackling Dynamic Problems with Multiobjective Evolutionary Algorithms. In: Knowles, J., Corne, D., Deb, K. (eds) Multiobjective Problem Solving from Nature. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72964-8_4
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