An Improved Immune Inspired Hyper-Heuristic for Combinatorial Optimisation Problems

Sim, K., Hart, E. (2014). An Improved Immune Inspired Hyper-Heuristic for Combinatorial Optimisation Problems. In: (Ed.) Proceedings of GECCO 2014 (Genetic and Evolutionary Computation Conference), , () ( ed.). (pp. ). : . ACM Digital Library.


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Abstract

The meta-dynamics of an immune-inspired optimisation sys- tem NELLI are considered. NELLI has previously shown to exhibit good performance when applied to a large set of optimisation problems by sustaining a network of novel heuristics. We address the mechanisms by which new heuristics are defined and subsequently generated. A new representation is defined, and a mutation-based operator inspired by clonal- selection introduced to control the balance between explo- ration and exploitation in the generation of new network elements. Experiments show significantly improved perfor- mance over the existing system in the bin-packing domain. New experiments in the job-scheduling domain further show the generality of the approach.
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Authors

Emma Hart
Director of CEC
e.hart@napier.ac.uk
+44 131 455 2783
Kevin Sim
Lecturer
K.Sim@napier.ac.uk
+44 131 455 2497

Areas of Expertise

Bio-inspired Computing
The Bio-Inspired Algorithms group within the Centre for Algorithms, Visualisation and Evolving Systems is a large and thriving group with interests in nature-inspired computing that include Evolutionary Computing, Hyper-Heuristics, Artificial Immune Systems and Swarm Intelligence.