Learning to generate combinatorial action sequences utilizing the initial sensitivity of deterministic dynamical systems

Authors:
Ryunosuke Nishimoto;Jun Tani
Affiliations:
The University of Tokyo, Tokyo, Japan and Brain Science Institute, RIKEN, Saitama, Saitama,;The University of Tokyo, Tokyo, Japan and Brain Science Institute, RIKEN, Saitama, Saitama,
Venue:
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
Year:
2003

Citing 5
Cited 1

On the run-time behaviour of stochastic local search algorithms for SAT

AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Cooperative Ant Colonies for Optimizing Resource Allocation in Transportation

Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
Information Exchange in Multi Colony Ant Algorithms

IPDPS '00 Proceedings of the 15 IPDPS 2000 Workshops on Parallel and Distributed Processing
A Parallel GRASP for MAX-SAT Problems

PARA '96 Proceedings of the Third International Workshop on Applied Parallel Computing, Industrial Computation and Optimization
Scatter Search with Random Walk Strategy for SAT and MAX-W-SAT Problems

Proceedings of the 14th International conference on Industrial and engineering applications of artificial intelligence and expert systems: engineering of intelligent systems

Motor primitive and sequence self-organization in a hierarchical recurrent neural network

Neural Networks - 2004 Special issue: New developments in self-organizing systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

The ant colony system or ACS is an important approach of the Ant Colony Optimization meta-heuristic. In this work we aim to study the efficiency of ACS in solving the weighted max-sat problem. This will require an adaptation of all the niles of this approach to the elements which characterize our problem. All the ACO algorithms contain a low dependence level, this feature makes them beneficent to parallelize. Thereby, we will also study various ways to parallelize the ACS algorithm proposed in the first step of this work and discuss their impacts and differences.