Cyclic Incrementality in Competitive Coevolution: Evolvability through Pseudo-Baldwinian Switching-Genes

Coevolving systems are notoriously difficult to understand. This is largely due to the Red Queen effect that dictates heterospecific fitness interdependence. In simulation studies of coevolving systems, master tournaments are often used to obtain more informed fitness measures by testing evolved individuals against past and future opponents. However, such tournaments still contain certain ambiguities. We introduce the use of a phenotypic cluster analysis to examine the distribution of opponent categories throughout an evolutionary sequence. This analysis, adopted from widespread usage in the bioinformatics community, can be applied to master tournament data. This allows us to construct behavior-based category trees, obtaining a hierarchical classification of phenotypes that are suspected to interleave during cyclic evolution. We use the cluster data to establish the existence of switching-genes that control opponent specialization, suggesting the retention of dormant genetic adaptations, that is, genetic memory. Our overarching goal is to reiterate how computer simulations may have importance to the broader understanding of evolutionary dynamics in general. We emphasize a further shift from a component-driven to an interaction-driven perspective in understanding coevolving systems. As yet, it is unclear how the sudden development of switching-genes relates to the gradual emergence of genetic adaptability. Likely, context genes gradually provide the appropriate genetic environment wherein the switching-gene effect can be exploited.

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Janssen, Rick
Nolfi, Stefano
Haselager, Pim
Sprinkhuizen-Kuyper, Ida
MIT Press,, Cambridge, MA , Stati Uniti d'America
Artificial life 22 (2016): 319–352. doi:10.1162/ARTL_a_00208
info:cnr-pdr/source/autori:Janssen, Rick; Nolfi, Stefano; Haselager, Pim; Sprinkhuizen-Kuyper, Ida/titolo:Cyclic Incrementality in Competitive Coevolution: Evolvability through Pseudo-Baldwinian Switching-Genes/doi:10.1162/ARTL_a_00208/rivista:Artificial
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