title: A computational model of symbiotic composition in evolutionary transitions.
abstract: Several of the major transitions in evolutionary history, such as the symbiogenic origin of eukaryotes from prokaryotes, share the feature that existing entities became the components of composite entities at a higher-level of organization. This composition of pre-adapted extant entities into a new whole is a fundamentally different source of variation from the gradual accumulation of small random variations, and it has some interesting consequences for issues of evolvability. Intuitively, the pre-adaptation of sets of features in reproductively independent specialists suggests a form of 'divide and conquer' decomposition of the adaptive domain. Moreover, the compositions resulting from one level may become the components for compositions at the next level, thus scaling-up the variation mechanism. In this paper, we explore and develop these concepts using a simple abstract model of symbiotic composition to examine its impact on evolvability. To exemplify the adaptive capacity of the composition model, we employ a scale-invariant fitness landscape exhibiting significant ruggedness at all scales. Whilst innovation by mutation and by conventional evolutionary algorithms becomes increasingly more difficult as evolution continues in this landscape, innovation by composition is not impeded as it discovers and assembles component entities through successive hierarchical levels.
authors.affiliation: Dynamical and Evolutionary Machine Organization, Volen Centre for Complex Systems, Brandeis University, Waltham, MA 02454, USA. richardw@cs.brandeis.edu
authors.forename: Richard A
authors.initials: RA
authors.lastname: Watson
authors.forename: Jordan B
authors.initials: JB
authors.lastname: Pollack
mesh_terms: D000222:Adaptation, Physiological / Q000235:genetics*; D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D017753:Ecosystem; D004843:Epistasis, Genetic; D014644:Genetic Variation; D006801:Humans; D008957:Models, Genetic; D011157:Population Dynamics; D012641:Selection, Genetic; D013559:Symbiosis / Q000235:genetics*