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Genetic Algorithm Applications for Challenging Real-World Problems: Some Recent Advances and Future Trends

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Applied Genetic Algorithm and Its Variants

Part of the book series: Springer Tracts in Nature-Inspired Computing ((STNIC))

Abstract

Originated from the work of J. Holland in the 70s, genetic algorithms have become one of the most popular and widely used computational developments for optimization and global search tasks. As a result, genetic algorithms have been intensively used in many scientific and engineering problems during the last few decades. Also, several variants of the original genetic algorithm approach have been defined in the literature, mostly tailored to cope with specialized real-world optimization problems in industry and other computer-assisted areas. In this context, this chapter offers a gentle description of the fundamentals and background of genetic algorithms. Then, it explores some of the most exciting recent advances regarding the application of genetic algorithms to challenging real-world problems in nonstandard fields, such as reverse engineering for manufacturing (curve and surface reconstruction from point clouds), medicine and bioinformatics (cancer prediction, detection, and diagnosis; cancer classification and treatment; and COVID-19), computer animation and video games (behavioral animation of NPCs for video games), and robotics (robot path planning). The chapter discusses how genetic algorithms can be successfully applied to address these problems, including several variants and/or hybridizations with other approaches for better performance. Some future trends toward the application of genetic algorithms in other potential fields and some current challenges for future work in the field are also outlined in this chapter.

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Acknowledgements

The authors would like to thank the financial support from the European Union’s Horizon 2020 research and innovation programme, Marie Sklodowska-Curie action, RISE program of the project PDE-GIR with grant agreement reference number 778035, and also from the Agencia Estatal de Investigación (AEI) of the Spanish Ministry of Science and Innovation, for the grant of the Computer Science National Program with reference number #PID2021-127073OB-I00 of the MCIN/AEI/10.13039/501100011033/FEDER, EU.

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  1. Department of Applied Mathematics and Computational Sciences, E.T.S.I. Caminos, Canales y Puertos, University of Cantabria, Avda. de los Castros, s/n, 39005, Santander, Spain

    Andrés Iglesias & Akemi Gálvez

  2. Faculty of Pharmaceutical Sciences, Toho University, 2-2-1 Miyama, 274-8510, Funabashi, Japan

    Andrés Iglesias & Akemi Gálvez

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  1. Andrés Iglesias
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  2. Akemi Gálvez
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Correspondence to Andrés Iglesias .

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  1. Department of Computer Science and Engineering, Techno International New Town, Kolkata, West Bengal, India

    Nilanjan Dey

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Iglesias, A., Gálvez, A. (2023). Genetic Algorithm Applications for Challenging Real-World Problems: Some Recent Advances and Future Trends. In: Dey, N. (eds) Applied Genetic Algorithm and Its Variants. Springer Tracts in Nature-Inspired Computing. Springer, Singapore. https://doi.org/10.1007/978-981-99-3428-7_2

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