Particle swarm optimization with deliberate loss of information

By C. Voglis, Konstantinos E. Parsopoulos, Isaac E. Lagaris. Soft Computing - A Fusion of Foundations, Methodologies and Applications, 2012

940 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Numerical Analysis

View Paper: https://www.semanticscholar.org/paper/18ae6a40f5b54d146f588e1b84c7fe9fa4b7c1f2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. L. Garamszegi
2. H. Schielzeth
3. N. Dochtermann
4. Roger Mundry
5. Shinichi Nakagawa
6. Daniel Haun
7. David Hodgson
8. S. Jenkins

In Numerical Analysis

1. Shinichi Nakagawa
2. Daniel Haun
3. Roger Mundry
4. Michael Tomasello
5. Marie Schäfer

Citations in the corpus

• A brief introduction to mixed effects modelling and multi-model inference in ecology (2018, methodology): “NHST and stepwise deletion have come under heavy criticism; they can overestimate the effect size of ‘significant’ predictors (Whittingham et al 2006; Forstmeier & Schielzeth 2011) and force the researcher to focus on a single best model as if it were the only combination of predictors with support…”
• Mate competition, testosterone and intersexual relationships in bonobos, Pan paniscus (2012, methodology): “We based our inference on full models (and present their results) to avoid false positives as well as biased effect size estimates and P values (Forstmeier & Schielzeth 2011).”
• Fair Is Not Fair Everywhere (2015, methodology): “To test the significance of the test variables overall (Forstmeier & Schielzeth, 2011) and of interactions and factors with multiple levels, we compared models with and without the respective effect using likelihoodratio tests (Dobson, 2010).”
• Statistical Issues and Assumptions of Phylogenetic Generalized Least Squares (2014, methodology): “Then, the R2 of the respective model is calculated and this, in turn, is used to derive the VIF, as VIF = 1/(1-R2) (see Fox and Monette 1992 for how factors with more than two levels are treated in this context).”
• Quantifying individual variation in behaviour: mixed-effect modelling approaches. (2013, background): “…to encourage working towards the most significant description of the data set, because step-wise approaches are statistically problematic (e.g. Whittingham et al. 2006; Forstmeier & Schielzeth 2011; Simmons, Nelson & Simonsohn 2011), and inhibit general inferences (Dochtermann & Jenkins 2011).”
• Data reliability in citizen science: learning curve and the effects of training method, volunteer background and experience on identification accuracy of insects visiting ivy flowers (2016, background): “…for only those insect groups where the sample size (the number of participants who encountered at least one insect of that category) was equal to 3 9 the number of predictors (main effects and interactions) or greater (Forstmeier & Schielzeth 2011), that is ≥18 (Experiment 1) or ≥9 (Experiment 2).”
• Particle Swarm Methods (2018, neutral): “Moreover, the dynamic of the Canonical PSO model motivated recent approaches that spare function evaluations by taking advantage of the particles’ dynamic [148].”
• Developing multiple hypotheses in behavioral ecology (2010, background): “As discussed earlier, with exploratory analyses extending interpretations beyond the data set analyzed can lead to spurious inferences (Hurvich and Tsai 1990 ;Z hang1992; Chatfield 1995; see Forstmeier and Schielzeth 2010 , t his issue, for discussion of additional statistical concerns).”
• Information-theoretic approaches to statistical analysis in behavioural ecology: an introduction (2010, background): “More discussion on the full-model approach can be found in Forstmeier and Schielzeth (2010) .”
• Chimpanzees use long-term spatial memory to monitor large fruit trees and remember feeding experiences across seasons (2013, background): “We considered the effect of individual predictors only if the initial full model reached significance (Forstmeier & Schielzeth 2011).”
• Nested by design: model fitting and interpretation in a mixed model era (2013, background): “It is important to consider the potential for type I errors when testing a larger number of data-level predictors (Forstmeier & Schielzeth 2011).”