1 / 8

Symbiotic Composition

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

saima
Télécharger la présentation

Symbiotic Composition

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Symbiotic Composition Watson & Pollack Summarized by KC Tsui

  2. 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)

  3. 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

  4. 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

  5. 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!

  6. 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

  7. 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

  8. 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)

More Related