Titlebook: Instance-Specific Algorithm Configuration; Yuri Malitsky Book 2014 Springer International Publishing Switzerland 2014 Adaptive algorithms.

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Related Work,tuning. Many techniques have been attempted to address this problem, including meta-heuristics, evolutionary computation, local search, etc. Yet despite the variability in the approaches, this flood of proposed work mainly ranges between four ideas: algorithm construction, instance-oblivious tuning,

sterilization

Instance-Specific Algorithm Configuration,vious tuning and instance-specific regression, while mitigating their weaknesses. Specifically, ISAC combines the two techniques to create a portfolio where each solver is tuned to tackle a specific type of problem instance in the training set. This is achieved using the assumption that problem inst

deciduous

Training Parameterized Solvers,he set covering problem (SCP), showing that instance-oblivious tuning of the parameters can yield significant performance improvements and that ISAC can perform better than an instance-specific regression approach. The second section presents the mixed integer problem (MIP) and shows that even a sta

Insubordinate

Indicative

Dynamic Training,of three steps (computing features, clustering, and training), this methodology is not restricted to any single approach for any of them. For example, so far we have shown how a local search, GGA, and selecting the single best solver in a portfolio are all possibilities for training a solver for a c

resuscitation

Training Parallel Solvers,e excelled in competitions in satisfiability (SAT), constraint programming (CP), and quantified Boolean formulae (QBF). Since around 2010, a new trend has emerged, namely the development of parallel solver portfolios. The obvious next step is to therefore consider dynamic parallel portfolios, i.e.,

投射

松軟無(wú)力

Evolving Instance-Specific Algorithm Configuration,t of problem types and domains. It is therefore necessary to develop algorithm portfolios, where, when confronted with a new instance, the solver selects the approach best suited for satisfying the desired objective. This process can then be further refined to intelligently create portfolios of dive

Ventricle