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Other operators such as if, min, max are also commonly used to evolve heuristics and have shown to be particularly useful when dealing with difficult scheduling objectives such as maximum tardiness and total weighted tardiness.
Behaviors are implemented as fuzzy controllers and the genetic algorithm is used to evolve their rules.
The interface velocity is used to evolve the level set function.
A 2-stage Runge Kutta method is used to evolve the solution in time.
TSA is used to evolve the structure and the parameter of fuzzy rule base.
These selected training samples are then used to evolve the network architecture efficiently.
Genetic programming (GP) is used to evolve secondary classifiers for disambiguating between pairs of handwritten digit images.
The concept is also used to evolve these networks to simpler designs with fewer matches for implementation in practice.
A 2-stage Runge Kutta method is used to evolve the solution forward in time, where the advective fluxes are part of the temporal integration.
In previous work, we demonstrated that genetic algorithms could be used to evolve dynamical neural networks for controlling the locomotion of a simulated hexapod agent.
The level set method is used to evolve a codimension-1 surface with fixed codimension-2 boundary in Rn under mean curvature flow.
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