School of Law, GD Goenka University’s Post

I'm delighted to have made a contribution in the publication in the prestigious book of Futuristic Trends in Scocial Sciences #Doesindianeedauniformcivilcode #IIPSERIES

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Just finished Thomas Kuhn's "The Structure of Scientific Revolutions," and wow, what an eye-opening read! Kuhn challenges the idea that #science is a straightforward journey. Instead, he shows that it’s shaped by #paradigms—the frameworks that guide our questions and research. Most of the time, scientists operate within these “normal” paradigms, solving established #puzzles. But when unexpected #anomalies arise, it can lead to a #crisis and a complete shift in #understanding—what he calls a scientific revolution. This really resonates with the #IBphilosophy, which encourages us to #think #critically and explore how knowledge evolves over time. It’s a powerful reminder that science is not just about facts; it’s a dynamic, human endeavor shaped by our context. If you're curious about the deeper nature of science, I highly recommend this book! #ThomasKuhn #NatureOfScience #IB #ScientificRevolutions #IBDP #Nature #of #science

Delighted 💐💐to share that our manuscript got published and available Online in “Heart and Mind” by Lippincott indexed in Scopus Q3 (IF: 0.7) and Web of Science (ESCI). Link: https://lnkd.in/gigXHB2i #deepveinthrombosis #physicalactivity #palliativecare #heartandmind #review #scopingreview

You simply need a research paper to inspire you to write another one 🥂 . Here it goes for Scopus https://lnkd.in/gPgNpbfH #scholarlywork#research

Benjamin Doerr Thank you for your post! Indeed, it is always good to remember this and to thank Kenneth Sörensen for strongly warning the (meta)heuristic research community about this dead-end. Practically, the main, if not only, key principle in the design of heuristics for combinatorial optimization is *Local Search*. Anything else is a variation of how you diversify and intensify the search (and we know there is no free lunch here). Consequently, I would be even more radical than Benjamin Doerr here: there’s no point in inventing a new nature-inspired algorithm, period! On the other hand, much work remains to be done to understand their practical efficiency. This is about math, and it's tough. We need more champion mathematicians and computer scientists working on this. Now, there is a general-purpose math solver that integrates much of what this research community has produced over the last 50 years: Hexaly, formerly LocalSolver. Whatever the problem is, consider benchmarking against it before publishing your own dedicated heuristic. Hexaly is FREE FOR ACADEMICS. So, there is no reason not to do it 😉

Stories are an integral part of science. I've always been interested in not only the stories that papers tell but also the backstage happenings and the makings of a paper. As there are an important window to: - How do people think of #science and #research? - How do they approach unknowns? - How does #research change who we are as people? - Is there a right way to doing research? I believe that the backstory is part of THE process and that in turn is integral to progress. 'All the best ideas come out of the process; they come out of the work itself.' - Painter Chuck Close and the 3-2-1 newsletter from James Clear In the link in the first comment, I share a bit of the story behind one of the recent papers from my group.

One of my research areas is #metaheuristics and there has been a lot (thousands) of articles written on this topic, but one of my go to authors in this space is Kenneth Sörensen. Here are five of his paper that provide a good overview of the area. The first article is one of my favourite articles. It's from 2015 (so almost ten years old). Alas, not a lot has changed in those years, but we should keep plugging away. #ORMS #OR Here are links to the papers: 1️⃣ https://lnkd.in/gnHtk_MC 2️⃣ https://lnkd.in/gyQGuXwz 3️⃣ https://lnkd.in/gPSi6GPJ 4️⃣ https://lnkd.in/gFbVHfCE 5️⃣ https://lnkd.in/gkrEtuTc or https://lnkd.in/gaBc--Aw

Good advice. You can seriously wreck your reputation if you propose some Tasmanian devil algorithm. But I admit that I feel torn about it. Two of my most impactful algorithmic ideas were inspired by other fields. The gender-based genetic algorithm (GGA) for algorithm configuration is the first example. We saw that a genetic algorithm should work differently when comparably few function evaluations can be afforded. This happens in nature as well. Bacteria create a new generation every 20 minutes or so. And there are a xillion of them. Mammals, for comparison, procreate from a handfull of weeks to a few years. And their numbers are often between millions to billions. And the genetic algorithm works fundamentally differently. This inspired us to develop GGA. The other meta-heuristic I co-developed is dialectic search, based on Hegel’s philosophy of dialectic. It was created to make the development of search heuristics for a given problem fundamentally easier for newcomers, by strictly separating the concerns of diversification and intensification. With GGA and dialectic search we won 22 first places at international competitions, and both algorithms power InsideOpt Seeker. But I fear if I was to publish either one of those original papers today, they would be rejected.

I would like a literature review that consists of two parts: 1. the balance betw