Symbiotic Composition Watson & Pollack Summarized by KC Tsui

Motivations Evolutionary Transition: symbiogenic origin of eukaryotes from proharyotes Improvement in performance by EA (operators) gets more difficult as the ‘evolution’ progresses Symbiotic composition pre-adaptation of sets of features Entities become components of composite entities at a higher level of organization (cf. AOC)

SEAM Symbiogenic Evolutionary Adaptation Model population of entities: an ecosystem of different species variation operator: a means for joining two entities in a symbiotic union fitness how stable is a union? overall fitness is a function of many context sensitive fitness Environment context: the biotic environment provided by other coevolving entities

SEAM (cont.) Create many different small entities While not end join a pair of entities (symbiotic composition) evaluate if resultant dominates the constituents in the current context keep else break end-if End-while

Symbiotic Composition A: --0---0----1---- B: 1-------01----0- A+B: 1-0---0-01-1--0- A: ----1----00-1-- B: --1-0---0-1---- A+B: --1-1---000-1-- ‘incomplete’ entities are created!

Fitness Evaluation Overall fitness is a weighted sum of context sensitive fitness Pareto dominance is used to determine stability Context is formed by combining features of other entities in the current population

Hierarchical-if-and-only-if (HIFF) landscape a hierarchically clustered structure Starts with a two-feature epistasis model Continue (recursively) adding other epistasis models Analogous to an recursive prisoners’ dilemma HIFF representative of self-organized dynamic systems – with ‘power law’ signatures

Questions Is HIFF generic enough to capture many problems such as TSP? How different is it from COEA? Reference Watson & Pollack, A Computational Model of Symbiotic Composition in Evolutionary Transitions, to appear in Biosystems Journal, 2002 (http://www.demo.cs.brandeis.edu/papers/long.html#biosystems_scet)