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Modernized Decision Making

An overview of Evolutionary Surrogate-assisted Prescription (ESP) as an AI-driven approach for improving decision-making across domains like business, engineering, science, and public policy. It explains how ESP combines historical data–based predictive models (surrogates) with evolutionary computation to explore and evaluate millions of possible strategies efficiently before applying them in the real world. The content highlights the ESP framework (consisting of a predictor and a prescriptor) and demonstrates its applications in data-efficient sequential decision-making and optimizing real-world challenges such as COVID-19 interventions, supported by examples, research, and interactive demos.

how evolutionary computation works

how evolutionary computation works

Evolution is the New Deep Learning

An overview of how evolutionary computation (EC) is emerging as a powerful complement to deep learning, emphasizing its ability to generate entirely new solutions through large-scale exploration and parallel search. It highlights key areas such as neuroevolution for optimizing neural network architectures, real-world commercialization in applications like web interface optimization, and advanced methods for solving complex optimization problems. Supported by research papers, interactive demos, and visualizations, it showcases the potential of EC to drive innovation across fields like AI, robotics, healthcare, and software design.

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