Ecological uses for genetic algorithms: predicting fish distributions in complex physical habitats

1995 ◽  
Vol 52 (9) ◽  
pp. 1893-1908 ◽  
Author(s):  
Donna J. D'Angelo ◽  
Judy L. Meyer ◽  
Leslie M. Howard ◽  
Stanley V. Gregory ◽  
Linda R. Ashkenas
Keyword(s):  
Genetic Algorithms ◽  
Control Strategies ◽  
Superior Performance ◽  
Statistical Techniques ◽  
Distribution Data ◽  
Regression Equations ◽  
Multivariate Statistical ◽  
Order Stream ◽  
Theory Of Evolution ◽  
New Information

Genetic algorithms (GA) are artificial intelligence techniques based on the theory of evolution that through the process of natural selection evolve formulae to solve problems or develop control strategies. We designed a GA to examine relationships between stream physical characteristics and trout distribution data for 3rd-, 5th-, and 7th-order stream sites in the Cascade Mountains, Oregon. Although traditional multivariate statistical techniques can perform this particular task, GAs are not constrained by assumptions of independence and linearity and therefore provide a useful alternative. To help gauge the effectiveness of the GA, we compared GA results with results from proportional trout distributions and multiple linear regression equations. The GA was a more effective predictor of trout distributions (paired t test, P < 0.05) than other methods and also provided new insights into relationships between stream geomorphology and trout distributions. Most importantly, GA equations emphasized the nonindependence of stream channel units by revealing that (i) the factors that influence trout distributions change along a downstream continuum, and (ii) channel unit sequence can be critical. Superior performance of the GA, along with the new information it provided, indicates that genetic algorithms may provide a useful alternative or supportive method to statistical techniques.

Some new Pythagorean fuzzy correlation techniques via statistical viewpoint with applications to decision-making problems

2021 ◽  
pp. 1-13
Author(s):  
Paul Augustine Ejegwa ◽  
Shiping Wen ◽  
Yuming Feng ◽  
Wei Zhang ◽  
Jia Chen
Keyword(s):  
Decision Making ◽  
Correlation Coefficient ◽  
Fuzzy Set ◽  
Intuitionistic Fuzzy Set ◽  
Superior Performance ◽  
Statistical Techniques ◽  
New Techniques ◽  
Reliable Technique ◽  
Performance Indexes ◽  
Fuzzy Correlation

Pythagorean fuzzy set is a reliable technique for soft computing because of its ability to curb indeterminate data when compare to intuitionistic fuzzy set. Among the several measuring tools in Pythagorean fuzzy environment, correlation coefficient is very vital since it has the capacity to measure interdependency and interrelationship between any two arbitrary Pythagorean fuzzy sets (PFSs). In Pythagorean fuzzy correlation coefficient, some techniques of calculating correlation coefficient of PFSs (CCPFSs) via statistical perspective have been proposed, however, with some limitations namely; (i) failure to incorporate all parameters of PFSs which lead to information loss, (ii) imprecise results, and (iii) less performance indexes. Sequel, this paper introduces some new statistical techniques of computing CCPFSs by using Pythagorean fuzzy variance and covariance which resolve the limitations with better performance indexes. The new techniques incorporate the three parameters of PFSs and defined within the range [-1, 1] to show the power of correlation between the PFSs and to indicate whether the PFSs under consideration are negatively or positively related. The validity of the new statistical techniques of computing CCPFSs is tested by considering some numerical examples, wherein the new techniques show superior performance indexes in contrast to the similar existing ones. To demonstrate the applicability of the new statistical techniques of computing CCPFSs, some multi-criteria decision-making problems (MCDM) involving medical diagnosis and pattern recognition problems are determined via the new techniques.

Assessing exercises recommended for women at risk of pelvic floor disorders using multivariate statistical techniques

2017 ◽  
Vol 29 (10) ◽  
pp. 1447-1454 ◽  
Author(s):  
Tania Tian ◽  
Stephanie Budgett ◽  
Jackie Smalldridge ◽  
Lynsey Hayward ◽  
James Stinear ◽  
...  
Keyword(s):  
At Risk ◽  
Pelvic Floor ◽  
Pelvic Floor Disorders ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical

Interactions of 2-PPM Lead in the Water Supply with Food Deprivation upon Maze-Swimming Behavior of Mice

1982 ◽  
Vol 55 (2) ◽  
pp. 515-519 ◽  
Author(s):  
S. Kowalski ◽  
G. H. Parker ◽  
M. A. Persinger
Keyword(s):  
Drinking Water ◽  
Water Supply ◽  
Food Deprivation ◽  
Tap Water ◽  
Swimming Behavior ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical ◽  
Food Condition ◽  
Ad Libitum

Mice that had been given either tap water or 2 ppm lead in their drinking water and either severely food deprived (3 days before testing) or allowed food ad libitum demonstrated significant interactions of lead treatment by day by food condition and lead by block. Although not statistically significant, the food deprived-lead treated mice displayed more errors and longer latencies than the ad libitum-water controls. The food deprived-water controls and ad libitum-lead-treated mice displayed intermediate values. The importance of using multivariate statistical techniques that can evaluate dynamic repeated behavioral measurements is emphasized.

Application of multivariable statistical techniques in plant-wide WWTP control strategies analysis

2007 ◽  
Vol 56 (6) ◽  
pp. 75-83 ◽  
Author(s):  
X. Flores ◽  
J. Comas ◽  
I.R. Roda ◽  
L. Jiménez ◽  
K.V. Gernaey
Keyword(s):  
Cluster Analysis ◽  
Control Strategies ◽  
Treatment Plant ◽  
Principal Component ◽  
Statistical Techniques ◽  
Data Sets ◽  
Data Set ◽  
Casual Relation ◽  
Evaluation Matrix ◽  
Natural Groups

The main objective of this paper is to present the application of selected multivariable statistical techniques in plant-wide wastewater treatment plant (WWTP) control strategies analysis. In this study, cluster analysis (CA), principal component analysis/factor analysis (PCA/FA) and discriminant analysis (DA) are applied to the evaluation matrix data set obtained by simulation of several control strategies applied to the plant-wide IWA Benchmark Simulation Model No 2 (BSM2). These techniques allow i) to determine natural groups or clusters of control strategies with a similar behaviour, ii) to find and interpret hidden, complex and casual relation features in the data set and iii) to identify important discriminant variables within the groups found by the cluster analysis. This study illustrates the usefulness of multivariable statistical techniques for both analysis and interpretation of the complex multicriteria data sets and allows an improved use of information for effective evaluation of control strategies.

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