Abstract
Many animals are able to modify their morphology during their lifetime in response to changes in the environment. Such modifications are often adaptive—they can improve individual’s chances of survival and reproduction. In this paper we explore the effects of such morphological plasticity on body-brain coevolution of virtual creatures. We propose a method where morphological plasticity is achieved through learning during individual’s lifetime allowing each individual to quickly adapt its morphology to the current environment. We show that the resulting plasticity allows evolution of creatures better adapted to different simulated environments. We also show that evolution combined with the new learning rule reduces the total computational cost required to evolve an individual with a given target fitness compared to evolution without learning.
This research was supported by SVV project number 260 333.
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- 1.
All boxplots use whisker bars for minimum and maximum value, box boundaries for 1st and 3rd quartile, horizontal line for the median and black dot for the mean.
- 2.
Preliminary experiments have shown that while longer learning phase further decreases the number of generations required to reach a given fitness value, it decreases the performance when the extra computational cost is also taken into account.
- 3.
Since learning increases the cost of each fitness evaluation by one third (from 48 s to 64s, see Fig. 4), the extra computational cost was accounted for by comparing results from generation 150 of evolution with learning with results from generation 200 of evolution without learning.
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Krcah, P. (2016). Adaptation of Virtual Creatures to Different Environments Through Morphological Plasticity. In: Tuci, E., Giagkos, A., Wilson, M., Hallam, J. (eds) From Animals to Animats 14. SAB 2016. Lecture Notes in Computer Science(), vol 9825. Springer, Cham. https://doi.org/10.1007/978-3-319-43488-9_11
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DOI: https://doi.org/10.1007/978-3-319-43488-9_11
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