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Publicação
III International conference on optimization, learning algorithms and applications
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| 3.43 MB | Adobe PDF |
Orientador(es)
Resumo(s)
Instance Space Analysis (ISA) is a recently developed methodology to support objective testing of algorithms. Rather than reporting algorithm performance on average across a chosen set of test problems, as is standard practice, ISA offers a more nuanced understanding of the unique strengths and weaknesses of algorithms across different regions of the instance space that may otherwise be hidden on average. It also facilitates objective assessment of any
bias in the chosen test instances, and provides guidance about the adequacy of benchmark test suites and the generation of more diverse and comprehensive test instances to span the instance space. This talk provides an overview of the ISA methodology, and the online software tools that are enabling its worldwide adoption in many disciplines. A case study comparing algorithms for university timetabling is presented to illustrate the methodology and tools, with several
other applications to optimization, machine learning, computer vision and quantum computing highlighted.
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Contexto Educativo
Citação
Pereira, Ana I.; Mendes, Armando; Fernandes, Florbela P.; Coelho, João Paulo; Teixeira, João Paulo; Cascalho, José; Lima, José; Pacheco, Maria F.; Lopes, Rui Pedro (Eds.) (2023). III International conference on optimization, learning algorithms and applications.