Surrogate assisted evolutionary many objective optimisation

This project aims to address the main challenges in evolutionary many-objective optimisation using model-based techniques and surrogate-assisted evolutionary optimisation. To this end, the objectives of the project include:

• Develop a model-based evolutionary algorithm, thereby making it easier to represent the Pareto-optimal solutions
• Develop a preference-based approach to guide the evolutionary search. In additional to the use of preference for modifying the dominance, an inverse model that can map the preferred search space in the objective space to the decision space will be constructed. With the help of the inverse model, the search can be biased toward the preferred region in the decision space. An on-line adaptation of the preferred solution will be considered
• In order to reduce computational time, surrogate models will be developed that predict the rank of the solutions
• The developed algorithms will be verified on a real-world design optimisation problems.