Accelerating evolutionary algorithms with Gaussian process fitness function models
We present an overview of evolutionary algorithms that use empirical models of the fitness function to accelerate convergence, distinguishing between evolution control and the surrogate approach. We describe the Gaussian process model and propose using it as an inexpensive fitness function surrogate. Implementation issues such as efficient and numerically stable computation, exploration versus exploitation, local modeling, multiple objectives and constraints, and failed evaluations are...[Show more]
|Collections||ANU Research Publications|
|Source:||IEEE Transactions on Systems, Man and Cybernetics. Part C: Applications and Reviews|
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