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 CompositionA: B:
A+B: A: B:
A+B:
‘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 (