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EMO
2001
Springer

Reducing Local Optima in Single-Objective Problems by Multi-objectivization

13 years 8 months ago
Reducing Local Optima in Single-Objective Problems by Multi-objectivization
One common characterization of how simple hill-climbing optimization methods can fail is that they become trapped in local optima - a state where no small modi cation of the current best solution will produce a solution that is better. This measure of `better' depends on the performance of the solution with respect to the single objective being optimized. In contrast, multi-objective optimization (MOO) involves the simultaneous optimization of a number of objectives. Accordingly, the multi-objective notion of `better' permits consideration of solutions that may be superior in one objective but not in another. Intuitively, we may say that this gives a hill-climber in multi-objective space more freedom to explore and less likelihood of becoming trapped. In this paper, we investigate this intuition by comparing the performance of simple hill-climber-style algorithms on single-objective problems and multiobjective versions of those same problems. Using an abstract buildingblock p...
Joshua D. Knowles, Richard A. Watson, David Corne
Added 28 Jul 2010
Updated 28 Jul 2010
Type Conference
Year 2001
Where EMO
Authors Joshua D. Knowles, Richard A. Watson, David Corne
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