The aim of the Computational Intelligence Research Initiative (CIRI) is to improve the comprehension of computational intelligence processes for everyone: researchers, domain experts, and those without any relevant experience. Computational intelligence processes are nature inspired, e.g. evolutionary algorithms, and can be used to address complex real-world problems where traditional approaches fail.
These processes often involve complex algorithms, and yield many trade-off solutions to a problem which a domain expert must choose from. We focus on enhancing the interaction between the user and the computational intelligence algorithm using interactive visualisations, to enable a user from any background to understand the state of the process. This research is led by Dr. Shahin Rostami and is a joint effort between members of the Machine Intelligence Group, Human Computer Interaction Group, Engineering of Social Informatics Research Group, and external collaborators from other universities.
Outreach is an important part of CIRI, and through public videos published on YouTube we aim to gain the interest of people from any background. These videos are intended for everyone, and as such no pre-existing knowledge of the subject or any scientific background is expected.
We also provide an open-source data-set, MooViz, to support the development of visualisation techniques for multi-objective optimisation.
- Rostami, Shahin, Ferrante Neri, and Michael G. Epitropakis. “Progressive Preference Articulation for Decision Making in Multi-Objective Optimisation Problems.” (2017).
- Rostami, Shahin, and Ferrante Neri. “A fast hypervolume driven selection mechanism for many-objective optimisation problems.” Swarm and Evolutionary Computation (2016).
- Rostami, Shahin, and Ferrante Neri. “Covariance matrix adaptation pareto archived evolution strategy with hypervolume-sorted adaptive grid algorithm.” Integrated Computer-Aided Engineering 23.4 (2016): 313-329.
- Rostami, Shahin, and Alex Shenfield. “A multi-tier adaptive grid algorithm for the evolutionary multi-objective optimisation of complex problems.” Soft Computing (2016): 1-17.
- Rostami, Shahin, et al. “A novel preference articulation operator for the evolutionary multi-objective optimisation of classifiers in concealed weapons detection.” Information Sciences 295 (2015): 494-520.
- Shenfield, Alex, and Shahin Rostami. “A multi objective approach to evolving artificial neural networks for coronary heart disease classification.” Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2015 IEEE Conference on. IEEE, 2015.
- Rostami, Shahin, and Alex Shenfield. “Cma-paes: Pareto archived evolution strategy using covariance matrix adaptation for multi-objective optimisation.” Computational Intelligence (UKCI), 2012 12th UK Workshop on. IEEE, 2012.
- Rostami, Shahin. Preference focussed many-objective evolutionary computation. Diss. Manchester Metropolitan University, 2014.