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| Authors: | Tamara Ulrich, Lothar Thiele |
| Group: | Computer Engineering |
| Type: | Inproceedings |
| Title: | Maximizing Population Diversity in Single-Objective Optimization |
| Year: | 2011 |
| Month: | July |
| Pub-Key: | ut2011a.pdf |
| Book Titel: | Genetic and Evolutionary Computation Conference (GECCO) |
| Pages: | 641-648 |
| Keywords: | Diversity in Decision Space, Evolutionary Algorithms |
| Publisher: | ACM |
| Abstract: | Typically, optimization attempts to find a solution which minimizes the given objective function. But often, it might also be useful to obtain a set of structurally very diverse solu- tions which all have acceptable objective values. With such a set, a decision maker would be given a choice of solutions to select from. In addition, he can learn about the optimization problem at hand by inspecting the diverse close-to-optimal solutions. This paper proposes NOAH, an evolutionary algorithm which solves a mixed multi-objective problem: Determine a maximally diverse set of solutions whose objective values are below a provided objective barrier. It does so by iter- atively switching between objective value and set-diversity optimization while automatically adapting a constraint on the objective value until it reaches the barrier. Tests on an nk-Landscapes problem and a 3-Sat problem as well as on a more realistic bridge construction problem show that the algorithm is able to produce high quality solutions with a significantly higher structural diversity than standard evo- lutionary algorithms. |
| Location: | Dublin, Ireland |
| Resources: | [BibTeX] [Paper as PDF] |
