Learning the Search Range for Evolutionary Optimization in Dynamic Environments |
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Authors: | E F Khor K C Tan T H Lee |
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Affiliation: | (1) Department of Electrical and Computer Engineering, National University of Singapore, Singapore, SG |
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Abstract: | Conventional evolutionary algorithms operate in a fixed search space with limiting parameter range, which is often predefined
via a priori knowledge or trial and error in order to ‘guess’ a suitable region comprising the global optimal solution. This
requirement is hard, if not impossible, to fulfil in many real-world optimization problems since there is often no clue of
where the desired solutions are located in these problems. Thus, this paper proposes an inductive–deductive learning approach
for single- and multi-objective evolutionary optimization. The method is capable of directing evolution towards more promising
search regions even if these regions are outside the initial predefined space. For problems where the global optimum is included
in the initial search space, it is capable of shrinking the search space dynamically for better resolution in genetic representation
to facilitate the evolutionary search towards more accurate optimal solutions. Validation results based on benchmark optimization
problems show that the proposed inductive–deductive learning is capable of handling different fitness landscapes as well as
distributing nondominated solutions uniformly along the final trade-offs in multi-objective optimization, even if there exist
many local optima in a high-dimensional search space or the global optimum is outside the predefined search region.
Received 15 January 2001 / Revised 8 June 2001 / Accepted in revised form 24 July 2001 |
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Keywords: | : Adaptive search space Evolutionary algorithms Inductive– deductive learning Multi-objective optimization |
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