Detecting background changes in environments with dynamic foreground by separating probability distribution function mixtures using Pearson's method of moments

Jenkins, C. F.; Jordan, Jay B.; Carlson, Jeff

doi:10.1117/12.704215

Cited by 1 publication

(3 citation statements)

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“…Change detection is of great importance in our real life, as evidenced in [11] and [8]. Changes, when occurring, are often difficult to be detected [11]. However, we find that all existing DMOPs created for algorithm testing and analysis in the DMO literature are far easy for any algorithm to detect the underlying environmental changes.…”

Section: Change Detectability Of Existing Dmopsmentioning

confidence: 97%

“…Change detection is of great importance in our real life, as evidenced in [11] and [8]. Changes, when occurring, are often difficult to be detected [11].…”

Section: Change Detectability Of Existing Dmopsmentioning

confidence: 99%

“…However, real-world problems have less detectable changing environments, e.g. fraud detection in e-banking services [8] or covert placement of threats in crowded public facilities [11]. In such dynamic environments, it is likely that the nondominated solutions discovered for the previous environment are no longer nondominated for the current environment.…”

Section: Introductionmentioning

confidence: 99%

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Less detectable environmental changes in dynamic multiobjective optimisation

Jiang

Kaiser

Guo

et al. 2018

Proceedings of the Genetic and Evolutionary Computation Conference

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Multiobjective optimisation in dynamic environments is challenging due to the presence of dynamics in the problems in question. Whilst much progress has been made in benchmarks and algorithm design for dynamic multiobjective optimisation, there is a lack of work on the detectability of environmental changes and how this affects the performance of evolutionary algorithms. This is not intentionally left blank but due to the unavailability of suitable test cases to study. To bridge the gap, this work presents several scenarios where environmental changes are less likely to be detected. Our experimental studies suggest that the less detectable environments pose a big challenge to evolutionary algorithms.

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Section: Change Detectability Of Existing Dmopsmentioning

confidence: 97%

“…Change detection is of great importance in our real life, as evidenced in [11] and [8]. Changes, when occurring, are often difficult to be detected [11].…”

Section: Change Detectability Of Existing Dmopsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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