Evolutionary Approaches to Improving the Layouts of Instance-Spaces
We propose two new methods for evolving the layout of an instance-space. Specifically we design three different fitness metrics that seek to: (i) reward layouts which place instances won by the same solver close in the space; (ii) reward layouts that place instances won by the same solver and where...
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Published in | Parallel Problem Solving from Nature – PPSN XVII pp. 207 - 219 |
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Main Authors | , |
Format | Book Chapter |
Language | English |
Published |
Cham
Springer International Publishing
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Series | Lecture Notes in Computer Science |
Subjects | |
Online Access | Get full text |
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Summary: | We propose two new methods for evolving the layout of an instance-space. Specifically we design three different fitness metrics that seek to: (i) reward layouts which place instances won by the same solver close in the space; (ii) reward layouts that place instances won by the same solver and where the solver has similar performance close together; (iii) simultaneously reward proximity in both class and distance by combining these into a single metric. Two optimisation algorithms that utilise these metrics to evolve a model which outputs the coordinates of instances in a 2d space are proposed: (1) a multi-tree version of GP (2) a neural network with the weights evolved using an evolution strategy. Experiments in the TSP domain show that both new methods are capable of generating layouts in which subsequent application of a classifier provides considerably improved accuracy when compared to existing projection techniques from the literature, with improvements of over 10% in some cases. Visualisation of the the evolved layouts demonstrates that they can capture some aspects of the performance gradients across the space and highlight regions of strong performance. |
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ISBN: | 9783031147135 3031147138 |
ISSN: | 0302-9743 1611-3349 |
DOI: | 10.1007/978-3-031-14714-2_15 |