An experimental study on the applicability of evolutionary algorithms to craniofacial superimposition in forensic identification

2009 ◽  
Vol 179 (23) ◽  
pp. 3998-4028 ◽  
Author(s):  
Oscar Ibáñez ◽  
Lucia Ballerini ◽  
Oscar Cordón ◽  
Sergio Damas ◽  
José Santamaría
Keyword(s):  
Experimental Study ◽  
Evolutionary Algorithms ◽  
Forensic Identification

scholarly journals An experimental study of adaptive control for evolutionary algorithms

2015 ◽  
Vol 35 ◽  
pp. 359-372 ◽  
Author(s):  
Giacomo di Tollo ◽  
Frédéric Lardeux ◽  
Jorge Maturana ◽  
Frédéric Saubion
Keyword(s):  
Experimental Study ◽  
Adaptive Control ◽  
Evolutionary Algorithms

Experimental Study of a Structured Differential Evolution with Mixed Strategies

2011 ◽  
Vol 15 (9) ◽  
pp. 1310-1319 ◽  
Author(s):  
Takashi Ishimizu ◽  
◽  
Kiyoharu Tagawa
Keyword(s):  
Experimental Study ◽  
Evolutionary Algorithms ◽  
Differential Evolution ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Benchmark Problems ◽  
Mixed Strategies ◽  
Ring Network ◽  
Wide Range ◽  
Torus Network

In this paper, a new Differential Evolution (DE) that has multiple populations, or islands, is proposed. The proposed DE is called Structured Differential Evolution (StDE). In order to generate a new individual from the current population, various characteristic strategies have been proposed for DE. However, the performances of these strategies depend on the kind of the optimization problem. The proposed StDE uses different strategies in respective islands. Therefore, it can be expected that the proposed StDE is effective for a wide range of optimization problems. Although various networks topologies among islands are reported for island-based evolutionary algorithms, the most popular ones, namely the ring network and the torus network, are employed by StDE. Furthermore, in order to enhance the performance of proposed StDE, various migration policies are examined in two kinds of networks though a variety of benchmark problems.

scholarly journals Evaluating Multiobjective Evolutionary Algorithms Using MCDM Methods

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Xiaobing Yu ◽  
YiQun Lu ◽  
Xianrui Yu
Keyword(s):  
Decision Making ◽  
Experimental Study ◽  
Evolutionary Algorithms ◽  
Performance Metrics ◽  
Multiple Criteria Decision Making ◽  
Multiple Criteria ◽  
Experimental Results ◽  
Multiobjective Evolutionary Algorithms

The evaluation of multiobjective evolutionary algorithms (MOEAs) involves many metrics, it can be considered as a multiple-criteria decision making (MCDM) problem. A framework is proposed to estimate MOEAs, in which six MOEAs, five performance metrics, and two MCDM methods are used. An experimental study is designed and thirteen benchmark functions are selected to validate the proposed framework. The experimental results have indicated that the framework is effective in evaluating MOEAs.

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