Adaptive search and the management of logistics systems. Base models for learning agents
type: Item
instance of: scholarly article
title: Adaptive search and the management of logistics systems. Base models for learning agents
zbMATH Open document ID: 0949.90005
author: C. Bierwirth
published in: Operations Research/Computer Science Interfaces Series
publication date: 2000-02-10T00:00:00Z
review text: The book is divided into two parts, the fundamentals and the application of evolutionary adaptive systems. The first part gives a general overview and is an introduction for a reader not familiar with the topic. The second part is focussed on a case study. Starting with an introduction into basic logistic problems several methods and techniques related to those used in the context of that case study are discussed. The first chapter is a detailed introduction on ``Computational Intelligence'' (CI) and its use as a learning technique in artificial systems. ``Artificial intelligence'' (AI), one of CI's roots, is examined first to show the difference between CI and AI. Chapter two introduces adaptive systems as a subclass of feedback control systems. Especially the figures used allow for an easy understanding of the different models. Methods to coordinate decentralised systems close chapter two. Chapter three outlines the basic ideas of genetic algorithms in the context of evolutionary algorithms, then deals with some modifications and extensions of the basic principles. The adaptive behaviour of genetic algorithms, especially the fitness of search spaces, in static environments is scrutinized in chapter four. The aim of that chapter is to allow for a prediction of the performance of evolutionary algorithms. Especially the convergence of several approaches is considered. In Chapter five the use of interacting computational systems which are able to adapt to changing environments -- so called ``adaptive agents'' -- is discussed. Their sensing, acting, communicating and (machine) learning are reflected. Chapter six (the first chapter of part 2) deals with some basic logistic problems and whether they have to be enhanced by an encoding-decoding system or learning operators to be feasible for evolutionary algorithms. Several ways to encode and decode solutions to scheduling problems for evolutionary algorithms are discussed in chapter seven. Later on the job shop problem --…
reviewed by: Peter Kischka
Mathematics Subject Classification ID: 90B06
Mathematics Subject Classification ID: 90-02
Mathematics Subject Classification ID: 90B35
zbMATH DE Number: 1402218
zbMATH Keywords: computational intelligence
zbMATH Keywords: evolutionary adaptive systems
zbMATH Keywords: feedback control
zbMATH Keywords: genetic algorithms
zbMATH Keywords: interacting computational systems
zbMATH Keywords: encoding-decoding system
zbMATH Keywords: learning operators
zbMATH Keywords: evolutionary algorithms
zbMATH Keywords: scheduling
zbMATH Keywords: job shop problem
zbMATH Keywords: customer jobs
MaRDI profile type: Publication
description: scientific article; zbMATH DE number 1402218