Guest editorial: boiling eggs in our genetic shoes

There was an old man of Thermopylæ, Who never did anything properly; But they said, “If you choose, To boil eggs in your shoes,You shall never remain in Thermopylæ.”[1]

It has been many years ago, to be precise over 20 years ago, when the first Genetic Algorithm issue of Materials and Manufacturing Processes[2] came out. The genetic doctrine for the non-biological materials system that we professed in its pages were, in those days, as Greek as Thermopylæ to an average member of the materials community, and the research strategies prescribed by us must have reminded them of the outrageous cooking recipe in one of the Edward Lear’s nonsense limericks that I have quoted in the beginning. Luckily for us Dr T.S. Sudarshan, the permanent editor of this journal, not only allowed us to use this platform for documenting the boiling processes of the eggs of our materials research in some weird genetic shoes, but he also allowed us to be indulged in this activity continuously for well over a decade now. I have documented the continuous progress of genetic algorithms in the materials domain in my recently published book,[3] where many papers published in this journal have indeed made some groundbreaking contributions. In the advent of state-of-the-art materials research, as we know it today, the chronicle of our genetic adventure firmly aided by this journal marches another step forward with the publication of this evolutionary computation issue.

The horror of the topsy-turvy Covid days, aided by my own superannuation in India[4] and the subsequent relocation to Czech Republic all aided to an inordinate delay in planning this latest issue on evolutionary computation. However, once we could finally manage to get started, the response from the global researchers in this area was quite overwhelming. Here, considering the current state of machine learning we have broadened the scope of this evolutionary computation issue. Besides evolutionary algorithms applied to materials and manufacturing, papers using other intelligent learning and modeling strategies with direct relevance to materials and manufacturing are included here as well. Therefore, this issue contains papers dealing with cellular automata, rule-based optimization, deep learning, and so on. In addition to this editorial, there are 21 research papers from some leading researchers in this field who are located in eight different countries: Czech Republic, Finland, India, Italy, Oman, Poland, Slovenia, and the USA. This time the papers will be distributed in two full issues of this journal.

Incidentally, while attending a major international materials conference recently, I realized that machine learning has suddenly become a buzzword in the materials arena. Being a bit pensive, I felt perhaps now time has come to remind the world that not today, but over 20 years ago, Dr T. S. Sudarshan and I realized that machine learning is of utmost importance in our engineering realm of interest, and that is why the first genetic algorithms issue of this journal was conceived and the tradition continues till date while, so to say in a figurative way, the materials research could venture out of the lore learnt in ancient Thermopylæ.

Let this issue be another step forward in futuristic machine learning.

Disclosure statement

No potential conflict of interest was reported by the authors.