In this study, the performance of three multi-objective selection choice function based hyper-heuristics that are combined with different move acceptance strategies including all-moves (AM), great deluge algorithm (GDA) and late acceptance (LA) are evaluated on the tri-objective the Walking Fish Group (WFG) test problems, which is as a common benchmark for multi-objective optimisation. The performance of our hyper-heuristics are compared to the well established multi-objective evolutionary algorithm; SPEA2.

Nowadays, the global education system is moving towards using
technology as a prime factor in the learning process. The United Kingdom
is the one of the pioneer countries, is concerned with e-learning and using
Virtual Learning Environments (VLEs). This project aimed to explore the
experience of British universities with use of VLEs, in particular Moodle.
The purpose of this project is not only to investigate the use of VLEs in
educational institutions in the UK, but also to investigate the use of VLEs

In this thesis, we investigate and develop a number of online learning
selection choice function based hyper-heuristic methodologies that attempt to
solve multi-objective unconstrained optimisation problems. For the first time,
we introduce an online learning selection choice function based hyperheuristic
framework for multi-objective optimisation. Our multi-objective
hyper-heuristic controls and combines the strengths of three well-known
multi-objective evolutionary algorithms (NSGAII, SPEA2, and MOGA), which

tA selection hyper-heuristic is a high level search methodology which operates over a fixed set of low levelheuristics. During the iterative search process, a heuristic is selected and applied to a candidate solutionin hand, producing a new solution which is then accepted or rejected at each step. Selection hyper-heuristics have been increasingly, and successfully, applied to single-objective optimization problems,while work on multi-objective selection hyper-heuristics is limited.

Hyper-heuristics are emerging methodologies that perform a search over the space of heuristics in an
attempt to solve difficult computational optimization problems. We present a learning selection choice
function based hyper-heuristic to solve multi-objective optimization problems. This high level approach
controls and combines the strengths of three well-known multi-objective evolutionary algorithms (i.e.
NSGAII, SPEA2 and MOGA), utilizing them as the low level heuristics. The performance of the proposed

Multi‐objective hyper‐heuristics is a search method or learning mechanism that operates over a fixed set of low‐level heuristics to solve multi‐objective optimization problems by controlling and combining the strengths of those heuristics. Although numerous papers on hyper‐heuristics have been published and several studies are still underway, most research has focused on single‐objective optimization. Work on hyper‐heuristics for multi‐objective optimization remains limited.