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From evolutionary computation to the evolution of things

Author

Listed:
  • Agoston E. Eiben

    (VU University Amsterdam)

  • Jim Smith

    (University of the West of England)

Abstract

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.

Suggested Citation

  • Agoston E. Eiben & Jim Smith, 2015. "From evolutionary computation to the evolution of things," Nature, Nature, vol. 521(7553), pages 476-482, May.
    • Handle: RePEc:nat:nature:v:521:y:2015:i:7553:d:10.1038_nature14544
    DOI: 10.1038/nature14544
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    Cited by:

    1. Salcedo-Sanz, S. & Cuadra, L., 2019. "Quasi scale-free geographically embedded networks over DLA-generated aggregates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1286-1305.
    2. Manuel Chica & Joaquín Bautista & Jesica de Armas, 2019. "Benefits of robust multiobjective optimization for flexible automotive assembly line balancing," Flexible Services and Manufacturing Journal, Springer, vol. 31(1), pages 75-103, March.
    3. Dong Liu & Zhihuai Xiao & Hongtao Li & Dong Liu & Xiao Hu & O.P. Malik, 2019. "Accurate Parameter Estimation of a Hydro-Turbine Regulation System Using Adaptive Fuzzy Particle Swarm Optimization," Energies, MDPI, vol. 12(20), pages 1-21, October.
    4. Jan Zrimec & Xiaozhi Fu & Azam Sheikh Muhammad & Christos Skrekas & Vykintas Jauniskis & Nora K. Speicher & Christoph S. Börlin & Vilhelm Verendel & Morteza Haghir Chehreghani & Devdatt Dubhashi & Ver, 2022. "Controlling gene expression with deep generative design of regulatory DNA," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    5. Fister, Iztok & Iglesias, Andres & Galvez, Akemi & Del Ser, Javier & Osaba, Eneko & Fister, Iztok & Perc, Matjaž & Slavinec, Mitja, 2019. "Novelty search for global optimization," Applied Mathematics and Computation, Elsevier, vol. 347(C), pages 865-881.
    6. Liu, Weilong & Zhang, Yong & Liu, Kailong & Quinn, Barry & Yang, Xingyu & Peng, Qiao, 2023. "Evolutionary multi-objective optimisation for large-scale portfolio selection with both random and uncertain returns," QBS Working Paper Series 2023/02, Queen's University Belfast, Queen's Business School.
    7. Gobeyn, Sacha & Mouton, Ans M. & Cord, Anna F. & Kaim, Andrea & Volk, Martin & Goethals, Peter L.M., 2019. "Evolutionary algorithms for species distribution modelling: A review in the context of machine learning," Ecological Modelling, Elsevier, vol. 392(C), pages 179-195.
    8. Luciano Ferreira Cruz & Flavia Bernardo Pinto & Lucas Camilotti & Angelo Marcio Oliveira Santanna & Roberto Zanetti Freire & Leandro Santos Coelho, 2022. "Improved multiobjective differential evolution with spherical pruning algorithm for optimizing 3D printing technology parametrization process," Annals of Operations Research, Springer, vol. 319(2), pages 1565-1587, December.
    9. Qian Zhu & Yongqing Li & Zhen Zhang, 2023. "Swarm Robots Search for Multiple Targets Based on Historical Optimal Weighting Grey Wolf Optimization," Mathematics, MDPI, vol. 11(12), pages 1-19, June.
    10. Behnam Kia & Allen Mendes & Akshay Parnami & Robin George & Kenneth Mobley & William L Ditto, 2020. "Nonlinear dynamics based machine learning: Utilizing dynamics-based flexibility of nonlinear circuits to implement different functions," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-17, March.
    11. Luo, Kaiping & Shen, Guangya & Li, Liheng & Sun, Jianfei, 2023. "0-1 mathematical programming models for flexible process planning," European Journal of Operational Research, Elsevier, vol. 308(3), pages 1160-1175.

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