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Cooperative Coevolutionary EA

Cooperative Coevolutionary EA. KC Tsui base on [Potter & De Jong 2000]. Objectives.

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Cooperative Coevolutionary EA

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  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 sdo 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 sdo 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) < 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.

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