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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 eu convention, ECAl 2009, held in Budapest, Hungary, in September 2009. The 141 revised complete papers offered 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, man made chemistry and neuroscience, crew choice, ecosystems and evolution, algorithms and evolutionary computation, philosophy and humanities, optimization, motion, and agent connectivity, and swarm intelligence.

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Additional info 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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G. AA vs BB) as would be more conventional in a single-species assortative grouping model (where relatedness and inclusive fitness concepts straightforwardly apply) (12). By using a poly-species model we can show that the process we model significantly increases the likelihood of reaching a higher-utility ESS even in cases where the basin of attraction for high-utility ESSs is initially very small (13). Note that we do not change the interaction coefficients between species but only change the co-location or interaction probability of species.

5 to 64 equilibrium by 50,000 updates with the population nearly evenly divided between leaders and followers. Lastly, we conducted treatments where we set the desired number of role-ids to be five and varied the distribution of the benefits among roles. Specifically, we conducted experiments where the benefits: (1) increased linearly; (2) where one role was rewarded significantly greater than the others; and (3), where the majority of task bonus values were penalties. For these experiments, the deme fitness function was the number of unique tasks performed.

For each individual, gn, initialise 2N associations: ai=n =1, ai≠n =0. (notes 1 & 2) Until (stopping-criterion) evolve species: For i from 1 to N: Ei=rand({0,1}). //create random context E. t=1. // counter to decide when to reinitialise the context. //Evaluate g in context E. For all, g, from Ecosystem in random order: E’=add(E,g). (note 2) Fit(g) = e(E’) - e(E). //Update environmental context If (Fit(g) > 0) then {E=E’. } else t++. If (t>T) {For i from 1 to N: Ei=rand({0,1}). } For each species s: s=reproduce(s).

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