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Evolutionary Computing
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Evolutionary computing (EC) is an exciting development in Computer Science. It amounts to building, applying and studying algorithms based on the Darwinian principles of natural selection. In this paper we briefly introduce the main concepts behind evolutionary computing. We present the main components all evolutionary algorithms (EA), sketch the differences between different types of EAs and survey application areas ranging from optimization, modeling and simulation to entertainment.
Forward citations
Cited by 2 Pith papers
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Optimizing quantum sensing networks via genetic algorithms and deep learning
Genetic algorithms optimize spin-network topologies for quantum magnetometry and reveal non-monotonic QFI scaling with size caused by crossover to classical behavior.
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Speeding up thermalization and quantum state preparation through engineered quantum collisions
Optimized sequences of qubit and qutrit ancillas, found by genetic algorithms, rapidly prepare single-mode cavity states including thermal, coherent, squeezed, and non-Gaussian targets.
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