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The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

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Published on: January 19, 2019

Social interaction as a heuristic for combinatorial optimization problems.

José F Fontanari1

  • 1Instituto de Física de São Carlos, Universidade de São Paulo, Caixa Postal 369, 13560-970 São Carlos, SP, Brazil.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|January 15, 2011
PubMed
Summary
This summary is machine-generated.

The Adaptive Culture Heuristic (ACH) can solve complex optimization problems. Computational cost scales with problem size F to the sixth power (F⁶) for reliable solutions.

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Assessment of Social Interaction Behaviors
06:41

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Assessment of Social Interaction Behaviors
06:41

Assessment of Social Interaction Behaviors

Published on: February 25, 2011

Area of Science:

  • Computational physics
  • Artificial intelligence
  • Complex systems

Background:

  • Axelrod's model simulates culture dissemination.
  • Optimization problems, like pattern classification, are computationally challenging.
  • Boolean Binary Perceptrons are used for binary pattern classification.

Purpose of the Study:

  • Evaluate the Adaptive Culture Heuristic (ACH) for solving NP-Complete problems.
  • Analyze the performance of ACH on Boolean Binary Perceptron pattern classification.
  • Determine the scaling of computational cost and success probability with problem size.

Main Methods:

  • Simulated N agents with binary strings of length F on a square lattice.
  • Agents interacted with nearest neighbors, adopting similar 'cultures' (solutions).
  • Observed system dynamics leading to a homogeneous absorbing state.

Main Results:

  • Success probability depends on the reduced variable F/N.
  • The number of agents N must scale with F⁴ for a fixed success probability.
  • Relaxation time to reach an absorbing state scales with F⁶.

Conclusions:

  • ACH can find optimal solutions for Boolean Binary Perceptron classification.
  • Computational cost for ACH scales as F⁶ for reliable optimization.
  • Agent population must grow as F⁴ to maintain a constant success rate.