An improved immune inspired hyper-heuristic for combinatorial optimisation problems
An improved immune inspired hyper-heuristic for combinatorial optimisation problems
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DOI：
10.1145/2576768.2598241
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发表时间：
2014-07
期刊：
影响因子：
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通讯作者：
Kevin Sim;E. Hart
中科院分区：
文献类型：
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作者：
Kevin Sim;E. Hart
元动力学的免疫启发优化系统NELLI被认为是。NELLI先前已经证明，通过维持一个新的算法网络，当应用于一个大型优化问题集时，表现出良好的性能。我们解决的机制，定义和随后产生的新的化学品。定义了一种新的表示法，并引入了一种受克隆选择启发的基于变异的算子来控制新网络元素生成过程中探索和利用之间的平衡。实验表明，显着提高了现有系统在装箱域的性能。作业调度领域的新实验进一步表明了该方法的通用性。
The meta-dynamics of an immune-inspired optimisation system 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 exploration and exploitation in the generation of new network elements. Experiments show significantly improved performance over the existing system in the bin-packing domain. New experiments in the job-scheduling domain further show the generality of the approach.