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|Title:||Evolutionary computation for multicomponent problems: opportunities and future directions|
|Citation:||Optimization in industry: present practices and future scopes, 2018 / Datta, S., Davim, J.P. (ed./s), Ch.2, pp.13-30|
|Publisher Place:||Cham, Switzerland|
|Series/Report no.:||Management and Industrial Engineering|
|Abstract:||Over the past 30 years many researchers in the field of evolutionary computation have put a lot of effort to introduce various approaches for solving hard problems. Most of these problems have been inspired by major industries so that solving them, by providing either optimal or near optimal solution, was of major significance. Indeed, this was a very promising trajectory as advances in these problem-solving approaches could result in adding values to major industries. In this paper we revisit this trajectory to find out whether the attempts that started three decades ago are still aligned with the same goal, as complexities of real-world problems increased significantly. We present some examples of modern real-world problems, discuss why they might be difficult to solve, and whether there is any mismatch between these examples and the problems that are investigated in the evolutionary computation area.|
|Keywords:||Multicomponent optimization; traveling thief problem; evolutionary algorithms; cooperative coevolution|
|Rights:||© Springer Nature Switzerland AG 2019|
|Appears in Collections:||Aurora harvest 4|
Computer Science publications
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