Advances in evolutionary computing. Theory and applications
type: Item
instance of: scholarly article
title: Advances in evolutionary computing. Theory and applications
zbMATH Open document ID: 1021.68065
DOI: 10.1007/978-3-642-18965-4
published in: Natural Computing Series
publication date: 2003-05-04T00:00:00Z
review text: Without coming into explanatory details that would remain inevitably inconsistent and incomplete, we expose directly the following abbreviations proposed by the editors of the book for the notions to be subsequently: GA = Genetic Algorithm; GP = Genetic Programming; EA = Evolutionary Algorithm; ES = Evolutionary Strategies; EP = Evolutionary Programming. The term Evolutionary Computing (EC hereafter) would comprise the foundations and applications of certain heuristic techniques based on the principles of natural evolution, and whose material object study should be the Evolutionary Algorithms (EAs), understood as computing devices that try to mimic the processes taking place in natural evolution. One can appreciate that \(\text{EC}= \text{GAs}\cup \text{GP}\cup \text{ES}\cup \text{EP}\). The present book offers a collection of 40 high-quality articles (chapters) on EC, grouped in two parts: (I) the theoretical aspects of EC (23 articles), and (II) applicational issues demonstrating the usefulness/success of EC methodologies (17 articles). Here there are some brief notes of the editors on each chapter (article) contents. In Part I (Theory), Chapter 1 (Smoothness, Ruggedness and Neutrality of Fitness Landscapes) studies the structures of fitness landscapes to provide a suitable mathematical framework for investigating the evolvability of complex systems. Chapter 2 (Fast EAs) tries to demonstrate the role of the search step in approximating the landscape by using different hybrid EAs. Chapter 3 (Visualizing EC) surveys the existing work on visualizing EAs, identifying some key issues for future research. New schemes or operator design are described in Chapters 4-5. Chapter 4 (New Schemes of Biologically Inspired EC) proposes a parameter free GA (Genetic Algorithm), based on the disparity theory of evolution which exploits different mutation rates of evolution population sizes. Chapter 5 (On the Design of Problem specific EAs) suggests guidelines for the design of genet…
reviewed by: Neculai Curteanu
Mathematics Subject Classification ID: 68Q85
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 68T20
Mathematics Subject Classification ID: 92B20
Mathematics Subject Classification ID: 92D99
Mathematics Subject Classification ID: 68-02
zbMATH DE Number: 1903743
zbMATH Keywords: Evolutionary computing
zbMATH Keywords: genetic algorithm
zbMATH Keywords: genetic programming
zbMATH Keywords: evolutionary algorithm
zbMATH Keywords: evolutionary strategies
zbMATH Keywords: evolutionary programming
zbMATH Keywords: timetabling
zbMATH Keywords: meachine learning
zbMATH Keywords: crossover operator
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1007/978-3-642-18965-4
OpenAlex ID: W2490377892
description: scientific article; zbMATH DE number 1903743