Evolutionary algorithms and their applications to engineering problems

By A. Słowik, Halina Kwasnicka. Neural computing & applications (Print), 2020

618 citations recorded by the source corpus.

The main focus of this paper is on the family of evolutionary algorithms and their real-life applications. We present the following algorithms: genetic algorithms, genetic programming, differential evolution, evolution strategies, and evolutionary programming. Each technique is presented in the pseudo-code form, which can be used for its easy implementation in any programming language. We present the main properties of each algorithm described in this paper. We also show many state-of-the-art practical applications and modifications of the early evolutionary methods. The open research issues are indicated for the family of evolutionary algorithms.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/10ba5ca278b74558affbf5246347f5b99ef111f2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Absalom El-Shamir Ezugwu
2. Jeffrey O. Agushaka
3. Laith Mohammad Abualigah
4. Ali Diabat

In Computational Theory and Mathematics

1. Jeffrey O. Agushaka
2. Laith Mohammad Abualigah
3. Absalom El-Shamir Ezugwu

Citations in the corpus

• Gazelle optimization algorithm: a novel nature-inspired metaheuristic optimizer (2022, background): “Many researchers have proposed novel or hybrid methods to find optimal solutions to these problems [15, 27, 29, 46, 51, 56, 58].”