Evolutionary computation comprises a family of metaheuristic algorithms inspired by the principles of natural evolution – reproduction, mutation, recombination, and selection – which are utilised to ...

Modern humans descended from not one, but at least two ancestral populations that drifted apart and later reconnected, long before modern humans spread across the globe. Using advanced analysis based ...

Constantly "re-rolling the dice", combining and selecting: "Evolutionary algorithms" mimic natural evolution in silico and lead to innovative solutions for complex problems. Constantly “re-rolling the ...

The growth and expansion of metropolitan areas has been evident over the past decade. Buildings are getting taller, and urban areas are getting larger. What if there was a way to predict this growth ...

This course will guide students on their own intellectual journey in evolutionary computation. Early lectures provide a jumping off point — an overview of genetic algorithms, evolutionary strategies, ...

At the intersection of neuroscience and artificial intelligence (AI) is an alternative approach to deep learning. Evolutionary algorithms (EA) are a subset of evolutionary computation—algorithms that ...

Dr. James McCaffrey of Microsoft Research explains stochastic gradient descent (SGD) neural network training, specifically implementing a bio-inspired optimization technique called differential ...