6533b827fe1ef96bd1286418

RESEARCH PRODUCT

Determining the Difficulty of Landscapes by PageRank Centrality in Local Optima Networks

Sebastian Herrmann

subject

Mathematical optimizationIterated local searchbusiness.industrymedia_common.quotation_subject02 engineering and technologyMachine learningcomputer.software_genreLocal optima networkslaw.inventionGlobal optimumPageRanklaw020204 information systems0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingQuality (business)Artificial intelligencebusinessCentralitycomputerMathematicsmedia_common

description

The contribution of this study is twofold: First, we show that we can predict the performance of Iterated Local Search (ILS) in different landscapes with the help of Local Optima Networks (LONs) with escape edges. As a predictor, we use the PageRank Centrality of the global optimum. Escape edges can be extracted with lower effort than the edges used in a previous study. Second, we show that the PageRank vector of a LON can be used to predict the solution quality (average fitness) achievable by ILS in different landscapes.

yearjournalcountryeditionlanguage
2016-01-01
https://doi.org/10.1007/978-3-319-30698-8_6