beneficial to real-world applications, local search structures have been proposed to drive the search towards the Pareto front more effectively and efficiently. A number of generic local search techniques have been proposed along with problem domain specific methods. These approaches are discussed in this chapter with thoughts on integrating new innovative local search with MOEAs. Another emerging area of MOEA research is applying coevolutionary techniques. Relatively few researchers have explored the idea of combining coevolution with MOEAs. This chapter presents various researchers’ algorithmic processes for Coevolutionary MOEAs (CMOEA) with each researcher’s efforts summarized, categorized, and analyzed. Some potential concept and future applications of MOEA coevolution are also suggested. Exercises, discussion questions, and possible research directions for MOEA local search and coevolution are presented at the end of the chapter.
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© 2007 Springer Science+Business Media, LLC
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(2007). MOEA Local Search and Coevolution. In: Evolutionary Algorithms for Solving Multi-Objective Problems. Genetic and Evolutionary Computation Series. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-36797-2_3
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DOI: https://doi.org/10.1007/978-0-387-36797-2_3
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-33254-3
Online ISBN: 978-0-387-36797-2
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