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• W855253555 abstract "Principal to the ideology behind hyper-heuristic research is the desire to increase thelevel of generality of heuristic procedures so that they can be easily applied to a widevariety of problems to produce solutions of adequate quality within practical timescales.This thesis examines hyper-heuristics within a single problem domain, thatof Bin Packing where the benefits to be gained from selecting or generating heuristicsfor large problem sets with widely differing characteristics is considered. Novelimplementations of both selective and generative hyper-heuristics are proposed. Theformer approach attempts to map the characteristics of a problem to the heuristic thatbest solves it while the latter uses Genetic Programming techniques to automate theheuristic design process. Results obtained using the selective approach show that solutionquality was improved significantly when contrasted to the performance of thebest single heuristic when applied to large sets of diverse problem instances. Althoughenforcing the benefits to be gained by selecting from a range of heuristics the studyalso highlighted the lack of diversity in human designed algorithms. Using GeneticProgramming techniques to automate the heuristic design process allowed both singleheuristics and collectives of heuristics to be generated that were shown to performsignificantly better than their human designed counterparts. The thesis concludes bycombining both selective and generative hyper-heuristic approaches into a novel immuneinspired system where heuristics that cover distinct areas of the problem spaceare generated. The system is shown to have a number of advantages over similar cooperativeapproaches in terms of its plasticity, efficiency and long term memory. Extensivetesting of all of the hyper-heuristics developed on large sets of both benchmarkand newly generated problem instances enforces the utility of hyper-heuristics in theirgoal of producing fast understandable procedures that give good quality solutions fora range of problems with widely varying characteristics." @default.
• W855253555 created "2016-06-24" @default.
• W855253555 creator A5059180650 @default.
• W855253555 date "2014-10-01" @default.
• W855253555 modified "2023-09-26" @default.
• W855253555 title "Novel hyper-heuristics applied to the domain of bin packing" @default.
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