#StackBounty: #optimization #genetic-algorithms #metaheuristics Ising Spin Glass – Optimization

Bounty: 50

I’m a newbie researcher working on model-based genetic algorithms, mainly linkage learning in both discrete and continuous spaces, using data modeling. I would like to ask you about Ising Spin Glass (ISG) problem in the context of optimization. I’ve seen a lot of papers running benchmarks on this particular problem. I’ve googled it, checked wiki but eventually understood nothing :/

  1. What is an intuitive definition of this problem, making it well suited for optimization?
  2. When referring to ISG problem in papers, which specific model researchers have in mind?
  3. How to formulate this problem in the context of optimization? (encoding, fitness evaluation)

I don’t ask nor expect a ready solution, some general direction would suffice as I feel kinda lost.

Update 1

I’ve found some python repo on github. It calculates energy in a following way:

import numpy as np

def totalEnergy(J, configuration):
    """
    calculate the energy for a given configuration.
    Parameters:
        >>> J: 
        nSpin*nSpin matrix, J[i,j] is the interation between spin i and j.
        >>> configuration: 
        1*nSpin vector, configuration[i] stores the spin at site i.
    Returns:
        the total energy of this configuration.
    """
    # 0.5 for double counting
    return 0.5*np.dot(configuration,np.dot(J,configuration))
```


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