Developing an optimisation algorithm Sample Clauses

Developing an optimisation algorithm. Although there are many different optimisation algorithms, each with various strengths and weaknesses, in MAENAD we focused on the use of particular forms of genetic algorithms to perform the multi-objective optimisation, following the HiP-HOPS optimisation technique. HiP- HOPS uses genetic algorithms to support optimisation with different discrete objectives (compared to some other techniques that merge objectives into a single evaluation figure, for instance) and thus create a set of Pareto optimal solutions. However, even for a given algorithm, there are many possible tweaks and variations available to enhance and optimise its performance and allow it to more efficiently explore the search space and obtain good solutions. Most optimisation algorithms have a variety of parameters that govern their usage, e.g. for genetic algorithms, the number of generations (i.e. iterations) and size of each population (i.e. set of solutions) can be tweaked, as can the rules governing the exploration of new solutions and the rules governing which solutions are kept. This work is ongoing, but in practice it may be that the optimisation parameters need to be tailored specifically to each model being optimised to produce the best results. +target * * +source * +mode * + condition: String EAElement Behav ior::Mode + takeRate: Float + genericConstraintType: GenericConstraintKind + genericConstraintValue: String Tak eRateConstraint TraceableSpecification GenericConstraint +genericConstraint ontext tSe t C GenericConstrain + category: String [0..1] + shortName: Identifier + uuid: String Identifiable::Identifiable
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