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| Content Provider | ACM Digital Library |
|---|---|
| Author | Fekete, Jean-Daniel Lutton, Evelyne |
| Abstract | An experimental analysis of evolutionary algorithms usually generates a huge amount of multidimensional data, including numeric and symbolic data. It is difficult to efficiently navigate in such a set of data, for instance to be able to tune the parameters or evaluate the efficiency of some operators. Usual features of existing EA visualisation systems consist in visualising time- or generation-dependent curves (fitness, diversity, or other statistics). When dealing with genomic information, the task becomes even more difficult, as a convenient visualisation strongly depends on the considered fitness landscape. In this latter case the raw data are usually sets of successive populations of points of a complex multidimensional space. The purpose of this paper is to evaluate the potential interest of a recent visual analytics tool for navigating in complex sets of EA data, and to sketch future developements of this tool, in order to better adapt it to the needs of EA experimental analysis. |
| Starting Page | 145 |
| Ending Page | 146 |
| Page Count | 2 |
| File Format | |
| ISBN | 9781450306904 |
| DOI | 10.1145/2001858.2001940 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2011-07-12 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Experimental analysis of eas Visual analytics Parameter tuning Evolutionary computation Fitness landscape visualisation |
| Content Type | Text |
| Resource Type | Article |
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