As the big data generated by the development of modern experiments and computing
technology becomes more and more accessible, the material design method based on …

With the development of the materials genome philosophy and data mining methodologies,
machine learning (ML) has been widely applied for discovering new materials in various …

Current trends in the progress of technology demand availability of materials resources
ahead of the advancing fronts of the application areas. During the last couple of decades …

This article provides a short introduction to the evolutionary multiobjective optimization field.
The first part of the article discusses the most representative multiobjective evolutionary …

The accurate prediction of tensile properties has great importance for the service life
assessment and alloy design of RAFM steels. In order to overcome the limitation of …

There is a growing interest in the inverse approach to material deign, in which the desired
target properties are used as input to identify the atomic identity, composition and structure …

Genetic algorithms (GAs) belong to the broader family of generic population based
metaheuristic optimisation algorithms called evolutionary algorithms. These are one of the …

Artificial neural network (ANN) models, correlating the mechanical properties (yield strength,
tensile strength,% elongation and r¯) of the cold rolled interstitial free (IF) steel sheets with …

With advancements in automation and high-throughput techniques, complex materials
discovery with multiple conflicting objectives can now be tackled in experimental labs. Given …

List of References on Evolutionary Multiobjective Optimization Page 1 List of References on
Evolutionary Multiobjective Optimization Carlos A. Coello Coello ccoello@cs.cinvestav.mx …