Introduction to artificial life
Introduction to artificial life
Evolution of genetic codes
Evolving Virtual Creatures and Catapults
Artificial Life
The effects of learning on the roles of chance, history and adaptation in evolving neural networks
ACAL'07 Proceedings of the 3rd Australian conference on Progress in artificial life
Hi-index | 0.00 |
We evolved multiple clones of populations of digital organisms to study the effects of chance, history, and adaptation in evolution. We show that clones adapted to a specific environment can adapt to new environments quickly and efficiently, although their history remains a significant factor in their fitness. Adaptation is most significant (and the effects of history less so) if the old and new environments are dissimilar. For more similar environments, adaptation is slower while history is more prominent. For both similar and dissimilar transfer environments, populations quickly lose the ability to perform computations (the analogue of beneficial chemical reactions) that are no longer rewarded in the new environment. Populations that developed few computational "genes" in their original environment were unable to acquire them in the new environment.