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.
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Cuccu, G., Gomez, F. (2011). When Novelty Is Not Enough. In: Di Chio, C., et al. Applications of Evolutionary Computation. EvoApplications 2011. Lecture Notes in Computer Science, vol 6624. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20525-5_24
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DOI: https://doi.org/10.1007/978-3-642-20525-5_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20524-8
Online ISBN: 978-3-642-20525-5
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