Abstract
The idea of evolving novel rather than fit solutions has recently been offered as a way to automatically discover the kind of complex solutions that exhibit truly intelligent behavior. So far, novelty search has only been studied in the context of problems where the number of possible "different" solutions has been limited. In this paper, we show, using a task with a much larger solution space, that selecting for novelty alone does not offer an advantage over fitness-based selection. In addition, we examine how the idea of novelty search can be used to sustain diversity and improve the performance of standard, fitness-based search. © 2011 Springer-Verlag.
Cite
CITATION STYLE
Cuccu, G., & Gomez, F. (2011). When novelty is not enough. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6624 LNCS, pp. 234–243). https://doi.org/10.1007/978-3-642-20525-5_24
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