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Hypervolume-Based Search for Multiobjective Optimization - Theory and Methods (Paperback)
Loot Price: R557
Discovery Miles 5 570
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Hypervolume-Based Search for Multiobjective Optimization - Theory and Methods (Paperback)
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Loot Price R557
Discovery Miles 5 570
Expected to ship within 10 - 15 working days
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Most problems encountered in practice involve the optimization of
multiple criteria. Usually, some of them are conflicting such that
no single solution is simultaneously optimal with respect to all
criteria, but instead many incomparable compromise solutions exist.
In recent years, evidence has accumulated showing that Evolutionary
Algorithms (EAs) are effective means of finding good approximate
solutions to such problems. One of the crucial parts of EAs
consists of repeatedly selecting suitable solutions. In this
process, the two key issues are as follows: first, a solution that
is better than another solution in all objectives should be
preferred over the latter. Second, the diversity of solutions
should be supported, whereby often user preference dictates what
constitutes a good diversity. The hypervolume offers one
possibility to achieve the two aspects; for this reason, it has
been gaining increasing importance in recent years. The present
thesis investigates three central topics of the hypervolume that
are still unsolved: 1: Although more and more EAs use the
hypervolume as selection criterion, the resulting distribution of
points favored by the hypervolume has scarcely been investigated so
far. Many studies only speculate about this question, and in parts
contradict one another. 2: The computational load of the
hypervolume calculation sharply increases the more criteria are
considered. This hindered so far the application of the hypervolume
to problems with more than about five criteria. 3: Often a crucial
aspect is to maximize the robustness of solutions, which is
characterized by how far the properties of a solution can
degenerate when implemented in practice. So far, no attempt has
been made to consider robustness of solutions within
hypervolume-based search.
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