Advances in Artificial Life. Darwin Meets von Neumann: 10th by Shelly X. Wu, Wolfgang Banzhaf (auth.), George Kampis,

By Shelly X. Wu, Wolfgang Banzhaf (auth.), George Kampis, István Karsai, Eörs Szathmáry (eds.)

The two-volume set LNAI 5777 and LNAI 5778 constitutes the completely refereed post-conference lawsuits of the tenth ecu convention, ECAl 2009, held in Budapest, Hungary, in September 2009. The 141 revised complete papers provided have been conscientiously reviewed and chosen from161 submissions. The papers are prepared in topical sections on evolutionary developmental biology and undefined, evolutionary robotics, protocells and prebiotic chemistry, structures biology, synthetic chemistry and neuroscience, workforce choice, ecosystems and evolution, algorithms and evolutionary computation, philosophy and humanities, optimization, motion, and agent connectivity, and swarm intelligence.

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Additional resources for Advances in Artificial Life. Darwin Meets von Neumann: 10th European Conference, ECAL 2009, Budapest, Hungary, September 13-16, 2009, Revised Selected Papers, Part II

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It can be seen that our mapping (RNA loop) reaches the most phenotypes (≈ 200 of 256), following the other RNA-based mappings (≈ 175 and 150), the random boolean network (145) and the cellular automaton (100). We can also see that the difference in the first steps is even more drastically, indicating a faster discovery rate of our mapping compared to all others. This can be explained by the connectivity, whereas CA and RBN have about 14 and 21 neighboring phenotypes, 250 100 200 80 RNA loop RNA full RNA distance RBN CA 150 100 25 60 40 20 50 0 # reached phenotypes # reached phenotypes A Sequence-to-Function Map for Ribozyme-Catalyzed Metabolisms 20 40 60 random walk steps (a) 80 100 0 5 10 random walk steps 15 20 (b) Fig.

2 An Ecosystem Model with Evolved Symbioses Our abstract model of an ecosystem contains 2N species, each of which contains P individuals. The fitness of each individual in each species will depend on the other species present in its local environmental context. A separation of timescales is crucial in this model (15): On the (fast) ecological dynamics timescale species densities within an environmental context change and quickly reach equilibrium, but on this timescale genetic changes are assumed to be negligible.

Specifically, the fitness of an individual genotype, g, belonging to species, s, given a context, E, is defined as fitness(g, E)= ∆e(E, s+S) = e(E+s+S) - e(E), where S is the set of species that g specifies as partners. Using the components introduced above, illustrated in Figure 1, our model operates as defined in Figure 2. Can Selfish Symbioses Effect Higher-Level Selection? e. s+S = -1--00-01-. For example, if this individual is placed into E= 1000100000, with e(E)=α. It will create E+S+s=1100000010, with e(E+S+s)=β.

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