This paper introduces Multi-population Ensemble Genetic Programming (MEGP), a computational intelligence framework that integrates cooperative coevolution and the multiview learning paradigm to address classification challenges in high-dimensional and heterogeneous feature spaces. MEGP decomposes the input space into conditionally independent feature subsets, enabling multiple subpopulations to evolve in parallel while interacting through a dynamic ensemble-based fitness mechanism. Each individual encodes multiple genes whose outputs are aggregated via a differentiable softmax-based weighting layer, enhancing both model interpretability and adaptive decision fusion. A hybrid selection mechanism incorporating both isolated and ensemble-level fitness promotes inter-population cooperation while preserving intra-population diversity. This dual-level evolutionary dynamic facilitates structured search exploration and reduces premature convergence. Experimental evaluations across eight benchmark datasets demonstrate that MEGP consistently outperforms a baseline GP model in terms of convergence behavior and generalization performance. Comprehensive statistical analyses validate significant improvements in Log-Loss, Precision, Recall, F1 score, and AUC. MEGP also exhibits robust diversity retention and accelerated fitness gains throughout evolution, highlighting its effectiveness for scalable, ensemble-driven evolutionary learning. By unifying population-based optimization, multi-view representation learning, and cooperative coevolution, MEGP contributes a structurally adaptive and interpretable framework that advances emerging directions in evolutionary machine learning.

In machine learning, the exponential growth of data and the associated ``curse of dimensionality'' pose significant challenges, particularly with expansive yet sparse datasets. Addressing these challenges, multi-view ensemble learning (MEL) has emerged as a transformative approach, with feature partitioning (FP) playing a pivotal role in constructing artificial views for MEL. Our study introduces the Semantic-Preserving Feature Partitioning (SPFP) algorithm, a novel method grounded in information theory. The SPFP algorithm effectively partitions datasets into multiple semantically consistent views, enhancing the MEL process. Through extensive experiments on eight real-world datasets, ranging from high-dimensional with limited instances to low-dimensional with high instances, our method demonstrates notable efficacy. It maintains model accuracy while significantly improving uncertainty measures in scenarios where high generalization performance is achievable. Conversely, it retains uncertainty metrics while enhancing accuracy where high generalization accuracy is less attainable. An effect size analysis further reveals that the SPFP algorithm outperforms benchmark models by large effect size and reduces computational demands through effective dimensionality reduction. The substantial effect sizes observed in most experiments underscore the algorithm's significant improvements in model performance.

Piction Health's app can help doctors classify a range of skin conditions. Piction Health, founded by Susan Conover SM '15, uses machine learning to help physicians identify and manage skin disease. When Susan Conover wanted to get a strange-looking mole checked out at the age of 22, she was told it would take three months to see a dermatologist. When the mole was finally removed and biopsied, doctors determined it was cancerous. At the time, no one could be sure the cancer hadn't spread to other parts of her body -- the critical difference between stage 2 and stage 3 or 4 melanoma.

Piction Health, founded by Susan Conover SM '15, uses machine learning to help physicians identify and manage skin disease. When Susan Conover wanted to get a strange-looking mole checked out at the age of 22, she was told it would take three months to see a dermatologist. When the mole was finally removed and biopsied, doctors determined it was cancerous. At the time, no one could be sure the cancer hadn't spread to other parts of her body -- the critical difference between stage 2 and stage 3 or 4 melanoma. Thankfully, it turned out that the mole ended up being confined to one spot. However, the experience launched Conover into the world of skin diseases and dermatology.

At the age of 22, when Susan Conover wanted to get a strange-looking mole checked out, she was told it would take three months to see a dermatologist. When the mole was finally removed and biopsied, doctors determined it was cancerous. At the time, no one could be sure the cancer hadn't spread to other parts of her body -- the difference between stage 2 and stage 3 or 4 melanoma. Thankfully, the mole ended up being confined to one spot. But the experience launched Conover into the world of skin diseases and dermatology.

This story is six years in the making, and it involves Zelda, Star Fox, another fox, College Humor, Netflix, Nintendo and Adam Conover. In February 2015, the Wall Street Journal reported Nintendo was putting together a live-action adaptation of the Legend of Zelda series for Netflix, described as "Game of Thrones for a family audience." The information came from an anonymous source close to the project. Other outlets covered the report, too -- but a Zelda Netflix show never materialized. Over the years, video game fans chalked it up to a crack in the rumor mill and moved on.

Automation is coming, pant the breathless pundits warning of A.I.-induced job loss. Ratcheting up the fear meter, presidential candidate Andrew Yang recently sounded the alarm for unprecedented employment gutting -- not just among blue-collar professions, but white-collar jobs, too. Meanwhile, renowned studies paint a gloomy picture, one in which rapid A. advances kneecap our middle-class dreams, sapping the hopes of young people who are left to wonder: Will there be a job for me when I graduate? And yet, the on-the-ground reality doesn't fit these sour prognostications. If anything, it offers good news for workers.