EURO 2025 Leeds
Abstract Submission

2079. A summary of quantum genetic algorithms and applications.

Invited abstract in session WB-16: Beyond the limits of QUBO formalism, stream Quantum OR .

Wednesday, 10:30-12:00
Room: Esther Simpson 2.07

Authors (first author is the speaker)

1. Mauricio Solar
Informatique, UTFSM

Abstract

There are several variants of the quantum genetic algorithm (QGA). One of them is the reduced quantum GA (RQGA), that removes the use of crossover, but also reduces computational complexity by assessing solutions simultaneously instead of iterating over populations as a QGA might do. RQGA makes extensive use of Grover's algorithm, used primarily when fitness is relatively easy to calculate. A Quantum Inspired GA (QIGA) is inspired by quantum mechanical principles, such as superposition, but does not make use of these principles in its implementation, performing better than a classical solution for a small domain. Hybrid Quantum GA (HQGA) combines classical techniques with the quantum paradigm, by doing crossover into a classical system but leaving the representation in a quantum form. This presentation wants to show the main strengths of an Improved Quantum GA (IQGA) over its classical variant. For example, the superposition allows several genetic combinations to be explored at once. Strategies proposed are the removal of crossover for IQGAs, as well as adding disasters within the genetic codes to re-introduce diversity. The characteristics that are associated with IQGA in the works that use it are: (1) Removing crossover; (2) Introducing population disasters; (3) Non-table based adaptive rotation operation.

Keywords

Status: accepted

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