Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing
Communications of the ACM
Parallel simulated annealing techniques
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 Physically Grounded Approach to Coordinate Movements in a Team
ICDCSW '02 Proceedings of the 22nd International Conference on Distributed Computing Systems
MAGMA: a multiagent architecture for metaheuristics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Logic Programming Techniques in Protein Structure Determination: Methodologies and Results
LPNMR '09 Proceedings of the 10th International Conference on Logic Programming and Nonmonotonic Reasoning
A distributed reinforcement learning approach for solving optimization problems
CIT'11 Proceedings of the 5th WSEAS international conference on Communications and information technology
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A protein is identified by a finite sequence of amino acids, each of them chosen from a set of 20 elements. The Protein Structure Prediction Problem is the problem of predicting the 3D native conformation of a protein, when its sequence of amino acids is known. Although it is accepted that the native state minimizes the free energy of the protein, all current mathematical models of the problem are affected by intrinsic computational limits, and moreover there is no common agreement on which is the most reliable energy function to be used. In this paper we present an agent-based framework for ab-initio simulations, composed by different levels of agents. Each amino acid of an input protein is viewed as an independent agent that communicates with the others. Then we have also strategic agents and cooperative ones. The framework allows a modular representation of the problem and it is easily extensible for further refinements and for different energy functions. Simulations at this level of abstraction allow fast calculation, distributed on each agent. We have written a multi-thread implementation, and tested the feasibility of the engine with two energy functions.