Quantum Inspired Genetic Algorithm


This project develops methods and software systems of quantum inspired evolutionary computation for the optimisation of parameters of intelligent systems, including spiking neural neural network systems.

Quantum- inspired evolutionary algorithm (QEA) is a new optimization technique which has combined quantum computing principles with evolutionary algorithms. QEA is a population based algorithm which uses the concepts of quantum bits and superposition of states as a basic rule to search the problem space. The smallest unit of information in QEA is called quantum bit or qubit which has three states ‘1’ and ‘0’, or any superposition of the two states. QEA has a better diversity and convergence rate in compare to other EAs because it uses qubit representation rather than numeric, binary or symbolic representations.

FIGURE. Geometrical representation of Qubit on Bloch Sphere.

We use this powerful optimization technique to optimize NeuCube performance in three different phases: encoding, unsupervised learning and supervised learning. Also, this algorithm will be use as a part of personalise modelling to improve feature selection ability of NeuCube.

Related Papers and Benchmarking

The proposed methods and systems, when compared with traditional statistical and optimisation methods, showed superior results in the following aspects:

  1. Better data analysis and classification/regression accuracy (by 10 to 40%);
  2. Better visualisation of the created models, with a possible use of VR;
  3. Better understanding of the data and the processes that are measured;
  4. Enabling new information and knowledge discovery through meaningful interpretation of the models;
  5. Significantly faster convergence.

See also some of the related papers:

Platel, M. D., Schliebs, S., & Kasabov, N. (2009). Quantum-inspired evolutionary algorithm: a multimodel EDA. IEEE Transactions on Evolutionary Computation, 13(6), 1218-1232.

Schliebs, S., Defoin-Platel, M., Worner, S., & Kasabov, N. (2009). Integrated feature and parameter optimization for an evolving spiking neural network: Exploring heterogeneous probabilistic models. Integrated  Neural Networks, 22(5), 623-632.

Schliebs, S., Kasabov, N., & Defoin-Platel, M. (2010). On the probabilistic optimization of spiking neural networks. International Journal of Neural Systems, 20(06), 481-500.

R&D System

For this project, an R&D system is being developed. The system can be obtained subject to licensing agreement.


The developer of this project is:

Helena Bahrami