Cooperative Coevolutionary EA

1
Cooperative Coevolutionary EA

• KC Tsui
• base on Potter De Jong 2000

2
Objectives

• The basic hypothesis to apply EA effectively
  to increasingly complex problems, explicit
  notions of modularity must be introduced for
  solutions to evolve in the form of interacting
  coadapted subcomponents

3
Major Issues

• Problem decomposition
• Interdependency of subcomponents
• Maintain diversity during search
• Credit assignment
• CCGA
• let decomposition emerges
• apply evolutionary pressure to force species to
  find their own niches

4
Basic Algorithm

• gen 0
• for each species s do begin
• Pops(gen) randomly initialized population
• evaluate fitness of each individual in Pops(gen)
• end
• while termination condition false do begin
• gen gen 1
• for each species s do begin
• select Pops (gen) from Pops (gen-1) based on
  fitness
• apply genetic operators to Pops (gen)
• evaluate fitness of each individual in Pops
  (gen)
• end
• end

5
Fitness Evaluation

• choose representative from each species
• FOR each individual i from S requiring evaluation
• BEGIN
• form collaboration between i and representatives
• evaluate collaboration by applying it to the
  target problem
• assign fitness of collaboration to i
• END

6
Variable of Species

• Add one species when the ecosystem is stagnated
• Initialize population randomly
• Evaluate fitness based on the overall fitness of
  the ecosystem
• Stagnation is defined by
• f(t) f(t-L) lt G, where
• f(t) is the fitness of best collaboration at time
  t an ecosystem generation
• L is a window size
• G is the threshold above which considerable
  amount of improvement has occurred
• Destroy the species that is not making enough
  contribution

7
Testbeds

• Function optimization
• Rules learning two species of rules
• String cover problem
• more species leads to higher improvement over
  canonical GA
• Stagnation/contribution measurement provides a
  good measure for the algorithm to adapt the
  number of species
• Cascade (neural) network architecture for the
  double spiral separation task

8
Related Papers

• Potter De Jong. (2000). Cooperative
  Coevolution An Architecture for Evolving
  Coadapted Subcomponents, Evolutionary Computation
  8(1) 1-29.
• Rosin Belew. (1997). New Methods for
  Competitive Coevolution, Evolutionary Computation
  5(1) 1-29.
• Moriatry Miikkulainen. (1998). Forming Neural
  Networks Through Efficient and Adaptive
  Coevolution, Evolutionary Computation 5(4)
  373-399.

9

Related Papers (2)

• Ficici Pollack. (2001). Game Theory and the
  Simple Coevolutionary Algorithm Some Preliminary
  Results on Fitness Sharing. GECCO 2001 Workshop
  on Coevolution Turning Adaptive Algorithms upon
  Themselves.
• Ficici Pollack. (2001). Pareto Optimality in
  Coevolutionary Learning. Sixth European
  Conference on Artificial Life, Jozef Kelemen
  (ed.), Springer, 2001.
• Watson Pollack. (2001). Coevolutionary Dynamics
  in a Minimal Substrate. Proceedings of the 2001
  Genetic and Evolutionary Computation Conference,
  Spector, L, et al (eds.), Morgan Kaufmann, 2001.