Abstract
EC is a class of nature-inspired algorithms that maintains a population of candidate solutions (individuals) and evolves toward the best answer(s). It has been frequently used to solve difficult real-world optimization problems since it evolves numerous solutions at the same time, which contribute to the notable characteristic of EC as being frequently insensitiveness to local minimal.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Janis, C. (1976). The evolutionary strategy of the equidae and the origins of rumen and cecal digestion. Evolution,30(4), 757–774.
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: Nsga-II. IEEE Transactions on Evolutionary Computation,6(2), 182–197.
Sun, Y., Yen, G. G., & Yi, Z. (2018b). IGD indicator-based evolutionary algorithm for many-objective optimization problems. IEEE Transactions on Evolutionary Computation. https://doi.org/10.1109/TEVC.2018.2791283.
Mitchell, M. (1996). An introduction to genetic algorithms. MIT Press.
Schmitt, L. M. (2001). Theory of genetic algorithms. Theoretical Computer Science,259(1-2), 1–61.
Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Conference on neural networks (Vol. 4). IEEE International.
Eberhart, R., & Kennedy, J. (1995). A new optimizer using particle swarm theory. In Proceedings of the Sixth International Symposium on Micro Machine and Human Science, 1995. MHS’95 (pp. 39–43). IEEE.
Storn, R., & Price, K. (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. https://doi.org/10.1023/A:1008202821328.
Price, K. V., Storn, R. A., & Lampinen, J. A. et al. (2005). Differential evolution: a practical approach to global optimization, Chapter 2 (pp. 37–42). Springer.
Walker, Matthew. (2001). Introduction to genetic programming. Tech. Np: University of Montana.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Sun, Y., Yen, G.G., Zhang, M. (2023). Evolutionary Computation. In: Evolutionary Deep Neural Architecture Search: Fundamentals, Methods, and Recent Advances. Studies in Computational Intelligence, vol 1070. Springer, Cham. https://doi.org/10.1007/978-3-031-16868-0_1
Download citation
DOI: https://doi.org/10.1007/978-3-031-16868-0_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-16867-3
Online ISBN: 978-3-031-16868-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)