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 …

Currently, the growth of material data from experiments and simulations is expanding
beyond processable amounts. This makes the development of new data‐driven methods for …

Currently, the challenge in front of researchers is to discover new novel material with
superior properties as per the demand of the society with a vast range of applications. With …

Genetic algorithms (GAs) are a tool used to solve high-complexity computational problems.
Apart from modelling the phenomena occurring in Nature, they help in optimization …

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 …

A method for finding the optimum alloy compositions considering a large number of property
requirements and constraints by systematic exploration of large composition spaces is …

Data-driven method utilizes machine learning (ML) to mine hidden rules in data, conforming
to the" fourth paradigm". A great deal of basic data is needed for this method. By comparing …

Steel design with the targeted properties is a challenging task due to the involvement of
many variables and their complex interactions. Artificial neural networks (ANN) recognized …

Darwinian and Lamarckian schemes within evolutionary algorithms have been implemented
and optimised. We compare the performance of these two approaches applied to the …