Abstract
Charles Darwin postulated the concept of “survival-of-the-fittest” and evolution in general. He discussed how nature selects the best candidate under different situations who are fit enough to survive and reproduce. This analogy has inspired many computational scientists, bioinformaticians, and computational biologists to develop techniques that can learn, adapt, and evolve to find optimal solutions for complex problems. Biologists are heavily dependent on computational methods and strategies to analyze humongous biological and medical data. Nature-inspired computing (NIC) encapsulates an ensemble of myriad studies of computer science, statistics, mathematics, and biological sciences where the essence is to adapt and develop robust competing techniques just like nature. It is a novel approach to optimization algorithms that are motivated by the dynamics of the biological evolution of our natural milieu. Over the past decade, various nature-inspired optimization algorithms have been deployed to solve complex problems in bioinformatics, engineering, and other sciences. With the glorious artificial intelligence (AI) revolution in biological sciences, there have been times when some problems are nonlinear in nature with multiple constraints and some techniques are hard to deploy. To solve high dimensionality issues and time complexity in such cases, NIC algorithms are the best choice to be used to solve complex optimization problems. This chapter highlights the commonly used NIC algorithms and their applications in biological sciences and bioinformatics.
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Abbreviations
- AAA:
-
Artificial algae algorithm
- AI:
-
Artificial intelligence
- AIS:
-
Artificial immune system
- CI:
-
Computational intelligence
- CSO:
-
Cat swarm optimization
- CSOA:
-
Chicken swarm optimization algorithm
- DE:
-
Differential evolution
- EA:
-
Evolutionary algorithms
- ESA:
-
Elephant search algorithm
- FSA:
-
Fish swarm algorithm
- GA:
-
Genetic algorithm
- GBC:
-
Genetic bee colony
- GP:
-
Genetic programming
- CGP:
-
Cartesian genetic programming
- GWO:
-
Grey wolf optimization
- MFO:
-
Moth flame optimization
- NIC:
-
Nature-inspired computing
- PSO:
-
Particle swarm optimization
- SI:
-
Swarm intelligence
- WOA:
-
Whale optimization algorithm
- WSN:
-
Wireless sensor networks
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SQ is supported by the DST-INSPIRE Fellowship provided by the Department of Science and Technology, Govt. of India.
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Qazi, W., Qazi, S., Iqbal, N., Raza, K. (2023). The Scope and Applications of Nature-Inspired Computing in Bioinformatics . In: Raza, K. (eds) Nature-Inspired Intelligent Computing Techniques in Bioinformatics. Studies in Computational Intelligence, vol 1066. Springer, Singapore. https://doi.org/10.1007/978-981-19-6379-7_1
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