1 / 9

Cooperative Coevolutionary EA

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

maili
Download Presentation

Cooperative Coevolutionary EA

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. 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.

More Related