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Progressively Adding Objectives: A Case Study in Anomaly Detection

Abstract : One of the principles of evolutionary multi-objective optimization is the conjoint optimization of the objective functions. However, in some cases, some of the objectives are easier to attain than others. This causes the population to lose diversity at a high rate and stagnate in early stages of the evolution. This paper presents the progressive addition of objectives (PAO) heuristic. PAO gradually adds objectives to a given problem relying on a perceived measure of complexity. This diversity loss phenomenon caused by the nature of a given objective has been observed when applying the Voronoi diagram-based evolutionary algorithm (VorEAl) in anomaly detection problems. Consequently, PAO has been first directed to address that issue. e experimental studies carried out show that the PAO heuristic manages to yield be er results than the direct use of VorEAl on a group of test problems.
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Contributor : Luis Martí Connect in order to contact the contributor
Submitted on : Wednesday, May 31, 2017 - 5:05:13 AM
Last modification on : Tuesday, October 19, 2021 - 10:53:31 PM
Long-term archiving on: : Wednesday, September 6, 2017 - 10:03:49 AM


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Luis Martí, Arsene Fansi-Tchango, Laurent Navarro, Marc Schoenauer. Progressively Adding Objectives: A Case Study in Anomaly Detection. Genetic and Evolutionary Computation Conference (GECCO 2017), Jul 2017, Berlin, Germany. ⟨10.1145/3071178.3071333⟩. ⟨hal-01525611⟩



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