Generating Single and Multiple Cooperative Heuristics for the One Dimensional Bin Packing Problem Using a Single Node Genetic Programming Island Model
Hart, E. (2013). Generating Single and Multiple Cooperative Heuristics for the One Dimensional Bin Packing Problem Using a Single Node Genetic Programming Island Model. In: Alba, E. (Ed.) Proceedgs of GECCO 2013, , () ( ed.). (pp. ). : . ACM SIGEVO.
Novel deterministic heuristics are generated using Single Node Genetic Programming for application to the One Dimensional Bin Packing Problem. First a single deterministic heuristic was evolved that minimised the total number of bins used when applied to a set of 685 training instances. Following this, a set of heuristics were evolved using a form of cooperative co-evolution that collectively minimise the number of bins used across the same set of problems. Results on an unseen test set comprising a further 685 problem instances show that the single evolved heuristic out- performs existing deterministic heuristics described in the literature. The collection of heuristics evolved by cooperative co-evolution outperforms any of the single heuristics, including the newly generated ones.
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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.