Evolutionary Algorithms

Opening — Why this matters now Topic modeling has matured into infrastructure. It quietly powers search, document clustering, policy analysis, and exploratory research pipelines across industries. Yet one deceptively simple question still wastes disproportionate time and compute: How many topics should my LDA model have? Most practitioners answer this the same way they did a decade ago: grid search, intuition, or vague heuristics (“try 50, see if it looks okay”). The paper behind this article takes a colder view. Selecting the number of topics, T, is not an art problem — it is a budget‑constrained black‑box optimization problem. Once framed that way, some uncomfortable truths emerge. ...

Opening — Why this matters now Reinforcement learning has always had an uncomfortable secret: most of the intelligence is smuggled in through the reward function. We talk about agents learning from experience, but in practice, someone—usually a tired engineer—decides what “good behavior” numerically means. As tasks grow longer-horizon, more compositional, and more brittle to specification errors, this arrangement stops scaling. ...

TL;DR Most “multi‑objective” solutions collapse trade‑offs into a single number. MORSE keeps the trade‑offs alive: it evolves a Pareto front of policies—not just solutions—so operators can switch policies in real time as priorities shift (profit ↔ emissions ↔ lead time). Add a CVaR knob and the system becomes tail‑risk aware, reducing catastrophic outcomes without babysitting. Why this matters (for operators & P&L owners) Supply chains live in tension: service levels vs working capital, speed vs emissions, resilience vs cost. Traditional methods either: ...

Traditionally, solving optimization problems involves meticulous human effort: crafting mathematical models, selecting appropriate algorithms, and painstakingly tuning hyperparameters. Despite the rigor, these human-centric processes are prone to bottlenecks, limiting the industrial adoption of cutting-edge optimization techniques. Wenhao Li and colleagues 1 challenge this paradigm in their recent paper, proposing an innovative shift toward evolutionary agentic workflows, powered by foundation models (FMs) and evolutionary algorithms. Understanding the Optimization Space Optimization problems typically traverse four interconnected spaces: ...