Genetic Algorithms and Genetic Programming

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A01=Andreas Beham
A01=Michael Affenzeller
A01=Stefan Wagner
A01=Stephan Winkler
advanced combinatorial optimisation methods
Author_Andreas Beham
Author_Michael Affenzeller
Author_Stefan Wagner
Author_Stephan Winkler
Building Block Hypothesis
Capacitated Vehicle Routing Problem
Category=UYQM
Combinatorial Optimization Problems
Crossover Operators
curve
Data Set
empirical algorithm evaluation
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
evolutionary computation
formula
GA Application
GA Theory
Genetic Algorithms
genetic programming
Global Optimal Solution
GP Population
GP Schema theories
GP Test
Heuristic lab
heuristic optimisation
Island Model
Linear Rank Selection
M2 M1
Melanoma Data Set
Michael Affenzeller
nonlinear modelling
Parallel GAs
parent
Parent Tours
population
population diversity
population dynamics analysis
roc
Roc Curve
Savings Heuristic
Schema Theorem
selection
Sequential GA
series
size
Solution Candidates
Standard GA
structure identification
test
Test Series
time series analysis
training
TSP Instance
VRP

Product details

  • ISBN 9781584886297
  • Weight: 680g
  • Dimensions: 156 x 234mm
  • Publication Date: 09 Apr 2009
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
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Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications discusses algorithmic developments in the context of genetic algorithms (GAs) and genetic programming (GP). It applies the algorithms to significant combinatorial optimization problems and describes structure identification using HeuristicLab as a platform for algorithm development.

The book focuses on both theoretical and empirical aspects. The theoretical sections explore the important and characteristic properties of the basic GA as well as main characteristics of the selected algorithmic extensions developed by the authors. In the empirical parts of the text, the authors apply GAs to two combinatorial optimization problems: the traveling salesman and capacitated vehicle routing problems. To highlight the properties of the algorithmic measures in the field of GP, they analyze GP-based nonlinear structure identification applied to time series and classification problems.

Written by core members of the HeuristicLab team, this book provides a better understanding of the basic workflow of GAs and GP, encouraging readers to establish new bionic, problem-independent theoretical concepts. By comparing the results of standard GA and GP implementation with several algorithmic extensions, it also shows how to substantially increase achievable solution quality.

Michael Affenzeller, Stefan Wagner, Stephan Winkler, Andreas Beham

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