Some techniques for analyzing time complexity of evolutionary algorithms

2011 ◽  
Vol 34 (6) ◽  
pp. 755-766 ◽  
Author(s):  
Lixin Ding ◽  
Jinghu Yu
Keyword(s):  
Evolutionary Algorithms ◽  
Time Complexity

Time Complexity Analysis of Evolutionary Algorithms on Random Satisfiable k-CNF Formulas

Algorithmica ◽  
2016 ◽  
Vol 78 (2) ◽  
pp. 561-586 ◽  
Author(s):  
Benjamin Doerr ◽  
Frank Neumann ◽  
Andrew M. Sutton
Keyword(s):  
Evolutionary Algorithms ◽  
Time Complexity ◽  
Complexity Analysis ◽  
Cnf Formulas

Opponent Center-Surround Contrast: Colour to Grey Conversion

2020 ◽  
Vol 2020 (1) ◽  
pp. 105-108
Author(s):  
Ali Alsam
Keyword(s):  
Evolutionary Algorithms ◽  
Three Dimensional ◽  
The State ◽  
Colour Space ◽  
Vision Science ◽  
State Of Art ◽  
Over Time

Vision is the science that informs us about the biological and evolutionary algorithms that our eyes, opticnerves and brains have chosen over time to see. This article is an attempt to solve the problem of colour to grey conversion, by borrowing ideas from vision science. We introduce an algorithm that measures contrast along the opponent colour directions and use the results to combine a three dimensional colour space into a grey. The results indicate that the proposed algorithm competes with the state of art algorithms.

Recognition of Birds in Blurred and Illumination Images by Local Features

2017 ◽  
Vol 7 (7) ◽  
pp. 243
Author(s):  
Suresha .M ◽  
. Sandeep
Keyword(s):  
Computer Vision ◽  
Time Complexity ◽  
Feature Detection ◽  
Feature Matching ◽  
Fast Algorithms ◽  
Local Features ◽  
Experimental Demonstration ◽  
Harris Corner ◽  
Blurred Image ◽  
Blurred Images

Local features are of great importance in computer vision. It performs feature detection and feature matching are two important tasks. In this paper concentrates on the problem of recognition of birds using local features. Investigation summarizes the local features SURF, FAST and HARRIS against blurred and illumination images. FAST and Harris corner algorithm have given less accuracy for blurred images. The SURF algorithm gives best result for blurred image because its identify strongest local features and time complexity is less and experimental demonstration shows that SURF algorithm is robust for blurred images and the FAST algorithms is suitable for images with illumination.

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