Computation at the edge of chaos: phase transitions and emergent computation
CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
A common framework for image segmentation
International Journal of Computer Vision
Different perspectives of the N-Queens problem
CSC '92 Proceedings of the 1992 ACM annual conference on Communications
Isomorphism and the N-Queens problem
ACM SIGCSE Bulletin
A hybrid neural approach to combinatorial optimization
Computers and Operations Research - Special issue: artificial intelligence, evolutionary programming and operations research
1/f fluctuation in the “Game of Life”
Physica D
A new kind of science
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Associative memory with dynamic synapses
Neural Computation
Manufacturing cell formation using a new self-organizing neural network
Computers and Industrial Engineering - 26th International conference on computers and industrial engineering
A self-organising neural network with intermittent switching dynamics for combinatorial optimisation
Design and application of hybrid intelligent systems
Performance-enhancing bifurcations in a self-organising neural network
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
Static and dynamic channel assignment using neural networks
IEEE Journal on Selected Areas in Communications
Neural techniques for combinatorial optimization with applications
IEEE Transactions on Neural Networks
A noisy self-organizing neural network with bifurcation dynamics for combinatorial optimization
IEEE Transactions on Neural Networks
Bifurcations of Renormalization Dynamics in Self-organizing Neural Networks
Neural Information Processing
On conditions for intermittent search in self-organizing neural networks
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
An evolutionary network model of epileptic phenomena
Neurocomputing
Critical temperatures for intermittent search in self-organizing neural networks
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
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One of the major obstacles in using neural networks to solve combinatorial optimization problems is the convergence toward one of the many local minima instead of the global minima. In this letter, we propose a technique that enables a self-organizing neural network to escape from local minima by virtue of the intermittency phenomenon. It gives rise to novel search dynamics that allow the system to visit multiple global minima as meta-stable states. Numerical experiments performed suggest that the phenomenon is a combined effect of Kohonen-type competitive learning and the iterated softmax function operating near bifurcation. The resultant intermittent search exhibits fractal characteristics when the optimization performance is at its peak in the form of 1/f signals in the time evolution of the cost, as well as power law distributions in the meta-stable solution states. TheN-Queens problem is used as an example to illustrate the meta-stable convergence process that sequentially generates, in a single run, 92 solutions to the 8-Queens problem and 4024 solutions to the 17-Queens problem.