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Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.3/221477
- Combining search space diagnostics and optimisation
- Moser, I.; Gheorghita, Marius
- Stochastic optimisers such as Evolutionary Algorithms outperform random search due to their ability to exploit gradients in the search landscape, formed by the algorithm's search operators in combination with the objective function. Research into the suitability of algorithmic approaches to problems bas been made more tangible by the direct study and characterisation of the underlying fitness landscapes. Authors have devised metrics, such as the autocorrelation length, to help define these landscapes. In this work, we contribute the Predictive Diagnostic Optimisation method, a new local-search based algorithm which provides knowledge about the search space while it searches for the global optimum of a problem. It is a contribution to a less researched area which may be named Diagnostic Optimisation.
- Publication type
- Conference paper
- Research centre
- Swinburne University of Technology
- Proceedings of the 2012 IEEE World Congress on Computational Intelligence (IEEE WCCI 2012), incorporating the 2012 International Joint Conference on Neural Networks (IJCNN 2012), the 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2012), and the 2012 IEEE Congress on Evolutionary Computation (IEEE CEC 2012), Brisbane, Queensland, Australia, 10-15 June 2012
- Publication year
- Algorithms; Diagnostic optimisation; Fitness landscapes; Optimisation; Predictive Diagnostic Optimisation method; Search space; Stochastic optimisers
- 9781467315098, 1467315095
- Publisher URL
- Copyright © 2012.
- Peer reviewed