Evolutionary algorithms and neural networks. Theory and applications
type: Item
instance of: scholarly article
title: Evolutionary algorithms and neural networks. Theory and applications
zbMATH Open document ID: 1400.68002
DOI: 10.1007/978-3-319-93025-1
published in: Studies in Computational Intelligence
publication date: 2018-07-18T00:00:00Z
review text: Evolutionary algorithms become a popular tool in finding optimal solutions for optimisation problems. They represent an alternative to the well-known Backpropagation algorithm. The evolutionary algorithms allow for the determination of a good architecture, setting the number of units and the number of hidden layers in the feed-forward networks. The search space may contain many sub-optimal solutions corresponding to local minima that the evolutionary algorithms may avoid. The book is composed of two parts. In the first part, evolutionary algorithms for optimisation are presented, and in the second part they are used to train neural networks. The nine chapters have the structure of scientific papers with a corresponding bibliography at the end. In the first chapter a general introduction to the evolutionary single-objective algorithms is presented where a global optimum for single-objective problems is searched. Constraints define which of the values for the input are valid. The difference between individual-based and population-based is highlighted. Exploration and exploitation are in conflict, a good balance in an algorithm is required. The search is done locally around the most promising solutions found in the exploration phase. Abrupt changes of the candidate solutions ensure that they explore different regions of space. In the following four chapters four popular evolutionary algorithms are presented. Particle swarm optimisation simulates the navigation of birds in flying swarms. Particles are allowed to move in a search landscape by the principles of separation, alignment and cohesion. In the next chapter the ant colony optimisation is introduced for combinatorial optimisation problems. The algorithm is based on stigmergy. Stigmergy refers to the manipulation of the environment by biological organisms to communicate. Routes of ants from a nest to a food source are marked by pheromones. The pheromone decrease is proportional to the length of the path. In chapter…
reviewed by: Andreas Wichert
Mathematics Subject Classification ID: 68-01
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 68T20
Mathematics Subject Classification ID: 90C59
zbMATH DE Number: 6905085
zbMATH Keywords: evolutionary algorithms
zbMATH Keywords: genetic algorithms
zbMATH Keywords: optimisation
zbMATH Keywords: neural networks
author: SeyedAli Mirjalili
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1007/978-3-319-93025-1
OpenAlex ID: W4250863246
description: scientific article; zbMATH DE number 6905085