Dear R users,
I am pleased to announce that QGA 1.0 is now available on CRAN.
QGA implements the Quantum Genetic Algorithm, as proposed by Han and Kim
in 2000, and is an R implementation derived from the Python one by
Lahoz-Beltra in 2016.
Under this approach, each solution is represented as a sequence of
(qu)bits. Simulating the quantum paradigm, these qubits are in a
superposition state: when measuring them, they collapse in a 0 or 1
state. After measurement, the solution's fitness is calculated as in
usual genetic algorithms.
The evolution at each iteration is oriented by the application of two
quantum gates to the amplitudes of the qubits: (1) a rotation gate
(always); (2) a Pauli-X gate (optionally). The rotation is based on the
theta angle values: higher values allow a quicker evolution, and lower
values avoid local maxima. The Pauli-X gate is equivalent to the
classical mutation operator and determines the swap between alfa and
beta amplitudes of a given qubit.
The package has been developed in such a way as to permit a complete
separation between the 'engine', and the particular problem subject to
combinatorial optimization. This is evident in the available examples,
that come with the package, illustrating the application of QGA to
different problems: knapsack, traveler salesman, and clustering.
Thank you, kind regards,
Giulio Barcaroli
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