Title : Constructing hybrid-meta-heuristics and hyper-heuristics for general optimization problem
Optimization problems relates to the process of finding the most maximum or the most minimum solution from a set of feasible solutions. In many cases, optimization problems can involve large search spaces, thus, hindering the use of exact and/or exhaustive search algorithms. As such, a compromise good enough solution offered by meta-heuristic algorithms is often sought after owing to timing and computational resource constraints. Despite being useful, typical meta-heuristic algorithms often lack generality and require problem specific modification before competitive solution can be obtained.
Hyper-heuristics offer an elegant generalization (and hybridization) of meta-heuristic algorithms. Viewed as heuristic to choose heuristics, hyper-heuristics automate the process of selecting and generating a set of heuristics for solving general optimization problems. A typical hyper-heuristic model has two levels, namely, the high level strategy for heuristic selection and acceptance as well as the low level (problem specific) heuristics (LLH). This tutorial explores the construction of hybrid meta-heuristic algorithms/hyper-heuristics from a set of LLHs or the whole meta-heuristics algorithms (e.g. PSO, Cuckoo, TLBO and Sine Cosine Algorithm to name a few). In particular, this tutorial also covers the construction of hybrid-meta-heuristics (including low level and high level hybrids) as well as hyper-heuristics (including that of Random Hyper-Heuristics, Choice Function Hyper-Heuristics, Tabu Search Hyper-Heuristics, and Monte Carlo Hyper-Heuristics).
|Early bird (Paid registration by 31st August 2019)||Student*||RM 150|
|Academician / Industry||RM 200|
|ICSECS Participant**||RM 120|
|Normal (1st September 2019 onwards)||Academician / Industry||RM 300|
** 20 seats free for registered (paid) ICSECS participants
What Will You Get
- Certificate of attendance
- Food will be provided (Breakfast, lunch and tea break)
- Source code of the algorithms
For more enquiry and assistance, please contact :
Dr. Mohd. Zamri Osman
Tel : 019-9126239