From evolutionary computation to the evolution of things

By Agoston E. Eiben, Jim E. Smith. 2015

460 citations recorded by the source corpus.

Evolution has provided a source of inspiration for algorithm designers since the birth of computers. The resulting field, evolutionary computation, has been successful in solving engineering tasks ranging in outlook from the molecular to the astronomical. Today, the field is entering a new phase as evolutionary algorithms that take place in hardware are developed, opening up new avenues towards autonomous machines that can adapt to their environment. We discuss how evolutionary computation compares with natural evolution and what its benefits are relative to other computing approaches, and we introduce the emerging area of artificial evolution in physical systems.

Subfields: Artificial Intelligence, Molecular Biology

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Citations in the corpus

Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (2017, background): “evolution in-silico in the field of evolutionary computation (Holland, 1975; Eiben and Smith, 2015).”
Evolution of Heuristics: Towards Efficient Automatic Algorithm Design Using Large Language Model (2024, unknown): “EC is a general optimization principle inspired by natural evolution (B¨ack et al., 1997; Eiben & Smith, 2015).”
Optimized flocking of autonomous drones in confined environments (2018, methodology): “To solve the optimization problem, we used evolutionary algorithms, population-based stochastic searchmethods inspired by natural evolution that have proven competitive in solving hard problems in the face of challenging characteristics such as nondifferentiability, discontinuities, multiple local o”
Quality and Diversity Optimization: A Unifying Modular Framework (2017, unknown): “These optimization abilities are also the core of evolutionary robotics in which evolutionary algorithms are used to generate neural networks, robot behaviors, or objects [9], [10].”
Evolutionary algorithms and their applications to engineering problems (2020, methodology): “Some research in the area of EAs can be connected with the so-called surrogate models (computationally cheaper models of real-world problems) which can be used in the place of full fitness evaluation, and that refine those models through occasional full evaluations of individuals in the population [”
Evolutionary Generative Adversarial Networks (2018, background): “tions, and select appropriate solutions according to fitness [56].”
A Tutorial On the design, experimentation and application of metaheuristic algorithms to real-World optimization problems (2021, methodology): “This momentum has been propelled by the emergence and progressive maturity of new paradigms related to problem modeling (e.g., large scale optimization, transfer optimization), as well as by the vibrant activity achieved in the Swarm Intelligence and Evolutionary Computation ﬁelds [3, 4, 5].”