A Jumping Gene Evolutionary Approach for Multiobjective Optimization

2012 ◽  
pp. 1-14
Author(s):  
Wallace K. S. Tang ◽  
Chris S. H. Yeung ◽  
K. F. Man
Keyword(s):  
Multiobjective Optimization ◽  
Evolutionary Approach

scholarly journals An Evolutionary Approach to Solve a Novel Mechatronic Multiobjective Optimization Problem

2007 ◽  
pp. 329-351 ◽  
Author(s):  
Efrén Mezura-Montes ◽  
Edgar A. Portilla-Flores ◽  
Carlos A. Coello Coello ◽  
Jaime Alvarez-Gallegos ◽  
Carlos A. Cruz-Villar
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Problem ◽  
Evolutionary Approach ◽  
Multiobjective Optimization Problem

Multiobjective Optimization of Transformer Design Using a Chaotic Evolutionary Approach

2014 ◽  
Vol 50 (2) ◽  
pp. 669-672 ◽  
Author(s):  
Leandro Dos S. Coelho ◽  
Viviana C. Mariani ◽  
Fabio A. Guerra ◽  
Mauricio V. F. da Luz ◽  
Jean V. Leite
Keyword(s):  
Multiobjective Optimization ◽  
Evolutionary Approach ◽  
Transformer Design

A New Evolutionary Approach to Multiparty Multiobjective Optimization

2021 ◽  
pp. 58-69
Author(s):  
Zeneng She ◽  
Wenjian Luo ◽  
Yatong Chang ◽  
Xin Lin ◽  
Ying Tan
Keyword(s):  
Multiobjective Optimization ◽  
Evolutionary Approach

Cognitive gadgets: A provocative but flawed manifesto

2019 ◽  
Vol 42 ◽  
Author(s):  
Marco Del Giudice
Keyword(s):  
Theory Of Mind ◽  
Cultural Differences ◽  
Twin Studies ◽  
Cross Cultural ◽  
Evolutionary Approach ◽  
Genetic Inheritance ◽  
Double Standards ◽  
Cross Cultural Differences ◽  
Cherry Picking ◽  
Dichotomous Thinking

Abstract The argument against innatism at the heart of Cognitive Gadgets is provocative but premature, and is vitiated by dichotomous thinking, interpretive double standards, and evidence cherry-picking. I illustrate my criticism by addressing the heritability of imitation and mindreading, the relevance of twin studies, and the meaning of cross-cultural differences in theory of mind development. Reaching an integrative understanding of genetic inheritance, plasticity, and learning is a formidable task that demands a more nuanced evolutionary approach.

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