On the Exploitation of Search History and Accumulative Sampling in Robust optimisation
Efficient robust optimisation methods exploit the search history when evaluating a new solution by using information from previously visited solutions that fall in the new solution's uncertainty neighbourhood. We propose a full exploitation of the search history by updating the robust fitness approximations across the entire search history rather than a fixed population. Our proposed method shows promising results on a range of test problems compared with other approaches from the literature.
The Artificial Bee Colony (ABC) is a recently introduced swarm intelligence algorithm for optimization, that has previously been applied successfully to the training of neural networks.
The Artificial Bee Colony (ABC) is a swarm intelligence algorithm for optimization that has previously been applied to the training of neural networks.
This paper empirically studies basic properties of the fitness landscape of random instances of number partitioning problem, with a focus on how these properties change with the phase transition.…