Abstract
This chapter introduces the concepts of evolutionary algorithms. The evolutionary algorithms are based on Darwin’s theory of evolution by natural selection. All the algorithms described in this chapter are conceptually based on this idea. Each algorithm interprets the idea in slightly different manner and proposes a different framework to solve certain type of problems. Specifically, we will discuss the following algorithms: (1) genetic algorithms, (2) simulated annealing, (3) ant colony optimization, and (4) swarm intelligence. These algorithms are aimed at using the biologically influenced techniques to improve the convergence of the optimization when the optimization problem is almost impossible to solve completely using most of the techniques described in other chapters.
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Notes
- 1.
In general all the algorithms that use gradient-based search are called as greedy algorithms. These algorithms use the fact from calculus that at any local optimum (minimum or maximum), the value of gradient is 0. In order to distinguish between whether the optimum is a minimum or a maximum, the second-order gradient is used. When the second-order gradient is positive, a minimum is reached; otherwise, it is a maximum.
- 2.
This problem belongs to a class of problems called as NP-hard. It stands for nondeterministic polynomial time hard problems [15]. The worst-case solution time for this problem increases in near-exponential time and quickly becomes beyond the scope of current hardware.
References
Travelling Salesman Problem https://en.wikipedia.org/wiki/Travellingsalesmanproblem
Genetic Programming API reference https://gplearn.readthedocs.io/en/stable/reference.html
Particle Swam Optimization Library https://pyswarms.readthedocs.io/en/latest/
S. Kirkpatrick, C.D. Gelatt Jr., M.P. Vecchi, Optimzation by Simulated Annealing, Science, New Series, Vol. 220, No. 4598, 1983.
Craig W. Reynolds Flocks, Herd and SChools: A Distributed Behavioral Model, Computer Graphics, 21(4), July 1987, pp 25-34.
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Joshi, A.V. (2023). Evolutionary Algorithms. In: Machine Learning and Artificial Intelligence. Springer, Cham. https://doi.org/10.1007/978-3-031-12282-8_11
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