Abstract
The chapter gives an introduction to optimization based on evolutionary computational techniques and swarm intelligence. Evolutionary computational algorithms adopt the principles of biological evolution and use a population of solutions that evolves with every generation. The bio-inspired computing algorithms that mimic the behavior of swarms of birds and insects, referred collectively as swarm intelligence, are a subset of evolutionary algorithms. The behavior of swarms individually as well as collective behavior in a flock has been extensively studied and an insight into their integration with the optimization algorithm is given. The evolutionary optimization algorithms such as genetic algorithm, particle swarm optimization, ant colony optimization, bee colony optimization, cuckoo search, fish school search, firefly algorithm have been reviewed. The application of these algorithms to image processing has been outlined, and few case studies have been presented.
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Vasuki, A. (2018). Certain Applications and Case Studies of Evolutionary Computing Techniques for Image Processing. In: Hemanth, J., Balas , V. (eds) Biologically Rationalized Computing Techniques For Image Processing Applications. Lecture Notes in Computational Vision and Biomechanics, vol 25. Springer, Cham. https://doi.org/10.1007/978-3-319-61316-1_12
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DOI: https://doi.org/10.1007/978-3-319-61316-1_12
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