Abstract
It is widely recognised that many species adapt to complex and dynamic environments, but it is no longer accepted that an organism passes characteristics acquired during its lifetime to its offspring. However, in evolutionary computation such Lamarckian inheritance can be useful. Simulations of the benefits of Lamarckian inheritance have been reported in the literature. However, in this paper we present the first formal proof that Lamarckian inheritance can dominate more traditional individual (non-inheritable) learning. We present a parameterised model that can demonstrate conditions in which different inheritance types perform best, which we empirically validate.
This work was supported by Science Foundation Ireland (Grant IN/05/I886).
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Curran, D., O’Sullivan, B. (2011). An Analysis of Lamarckian Learning in Changing Environments. In: Kampis, G., Karsai, I., Szathmáry, E. (eds) Advances in Artificial Life. Darwin Meets von Neumann. ECAL 2009. Lecture Notes in Computer Science(), vol 5778. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21314-4_18
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DOI: https://doi.org/10.1007/978-3-642-21314-4_18
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