Paper Avialable

[Shin Ishii]

Dear Connectionists,

I am pleased to inform you that the following paper is available
on my Web site:

	http://www.aist-nara.ac.jp/~ishii/publication.html

We would greatly appreciate it, if you could give us any comments
and suggestion.

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Lambda-opt neural approaches to quadratic assignment problems

Shin Ishii and Hirotaka Niitsuma

	to appear in Neural Computation

Abstract:
In this paper, we propose new analog neural approaches to
combinatorial optimization problems, in particular, quadratic
assignment problems (QAPs). Our proposed methods are based on an
analog version of the lambda-opt heuristics, which simultaneously
changes assignments for lambda elements in a permutation. Since
we can take a relatively large lambda value, our new methods can
achieve a middle-range search over possible solutions, and this
helps the system neglect shallow local minima and escape from
local minima. In experiments, we have applied our methods to
relatively large-scale (N = 80-150) QAPs.
Results have shown that our new methods are comparable to the
present champion algorithms; for two benchmark problems, they are
able to obtain better solutions than the previous champion
algorithms.

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Shin Ishii, Ph.D.
Nara Institute of Science and Technology
ATR Human Information Processing Research Laboratories