Cellular Genetic Algorithms

By: Enrique Alba, Bernabe Dorronsoro

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Hardcover | 6 June 2008

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264 Pages

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23.5 x 15.24 x 1.91

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Cellular Genetic Algorithms defines a new class of optimization algorithms based on the concepts of structured populations and Genetic Algorithms (GAs). The authors explain and demonstrate the validity of these cellular genetic algorithms throughout the book. This class of genetic algorithms is shown to produce impressive results on a whole range of domains, including complex problems that are epistatic, multi-modal, deceptive, discrete, continuous, multi-objective, and random in nature. The focus of this book is twofold. On the one hand, the authors present new algorithmic models and extensions to the basic class of Cellular GAs in order to tackle complex problems more efficiently. On the other hand, practical real world tasks are successfully faced by applying Cellular GA methodologies to produce workable solutions of real-world applications. These methods can include local search (memetic algorithms), cooperation, parallelism, multi-objective, estimations of distributions, and self-adaptive ideas to extend their applicability.

The methods are benchmarked against well-known metaheuristics like Genetic Algorithms, Tabu Search, heterogeneous GAs, Estimation of Distribution Algorithms, etc. Also, a publicly available software tool is offered to reduce the learning curve in applying these techniques. The three final chapters will use the classic problem of "vehicle routing" and the hot topics of "ad-hoc mobile networks" and "DNA genome sequencing" to clearly illustrate and demonstrate the power and utility of these algorithms.

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Introduction
Introduction to Cellular Genetic Algorithms	p. 3
Optimization and Advanced Algorithms	p. 4
Solving Problems Using Metaheuristics	p. 6
Evolutionary Algorithms	p. 7
Decentralized Evolutionary Algorithms	p. 11
Cellular Evolutionary Algorithms	p. 13
Synchronous and Asynchronous cEAs	p. 16
Formal Characterization of the Population in cEAs	p. 17
Cellular Genetic Algorithms	p. 18
Conclusions	p. 20
The State of the Art in Cellular Evolutionary Algorithms	p. 21
Cellular EAs: a New Algorithmic Model	p. 21
The Research in the Theory of the Cellular Models	p. 22
Characterizing the Behavior of cEAs	p. 24
The Influence of the Ratio	p. 26
Empirical Studies on the Behavior of cEAs	p. 26
Algorithmic Improvements to the Canonical Model	p. 29
Parallel Models of cEAs	p. 31
Conclusions	p. 34
Characterizing Cellular Genetic Algorithms
On the Effects of Structuring the Population	p. 37
Non-decentralized GAs	p. 37
Steady State GA	p. 38
Generational GA	p. 38
Decentralized GAs	p. 39
Experimental Comparison	p. 40
Cellular versus Panmictic GAs	p. 41
Cellular versus Distributed GAs	p. 43
Conclusions	p. 46
Some Theory: A Selection Pressure Study on cGAs	p. 47
The Selection Pressure	p. 48
Theoretical Study	p. 50
Approach to the Deterministic Model	p. 50
A Probabilistic Model for Approaching the Selection Pressure Curve	p. 52
Comparison of the Main Existing Mathematical Models	p. 57
Validation of the Theoretical Models	p. 60
Validation on Combinatorial Optimization	p. 61
Validation on Continuous Optimization	p. 65
Conclusions	p. 68
Algorithmic Models and Extensions
Algorithmic and Experimental Design	p. 73
Proposal of New Efficient Models	p. 73
Evaluation of the Results	p. 76
The Mono-objective Case	p. 77
The Multi-objective Case	p. 78
Some Additional Definitions	p. 80
Conclusions	p. 82
Design of Self-adaptive cGAs	p. 83
Introduction	p. 83
Description of Algorithms	p. 84
Static and Pre-Programmed Algorithms	p. 86
Self-Adaptive Algorithms	p. 87
Experimentation	p. 90
Parameterization	p. 91
Experimental Results	p. 92
Additional Discussion	p. 95
Conclusions	p. 99
Design of Cellular Memetic Algorithms	p. 101
Cellular Memetic Algorithms	p. 102
Simple and Advanced Components in Cellular MAs	p. 103
Three Basic Local Search Techniques for SAT	p. 103
Cellular Memetic GAs	p. 106
Computational Analysis	p. 107
Effects of Combining a Structured Population and an Adaptive Fitness Function (SAW)	p. 107
Results: Non Memetic Procedures for SAT	p. 109
Results: Cellular Memetic Algorithms	p. 110
Comparison Versus Other Algorithms in the Literature	p. 113
Conclusions	p. 114
Design of Parallel Cellular Genetic Algorithms	p. 115
The Meta-cellular Genetic Algorithm	p. 116
Parameterization	p. 117
Analysis of Results	p. 117
The Distributed Cellular Genetic Algorithm	p. 119
Parameterization	p. 120
Analysis of Results	p. 123
Conclusions	p. 125
Designing Cellular Genetic Algorithms for Multi-objective Optimization	p. 127
Background on Multi-objective Optimization	p. 129
The MOCell Algorithm	p. 130
Extensions to MOCell	p. 132
Experimental Analysis	p. 133
Conclusions	p. 138
Other Cellular Models	p. 139
Hierarchical cGAs	p. 139
Hierarchy	p. 140
Dissimilarity Selection	p. 141
First Theoretical Results: Takeover Times	p. 142
Computational Experiments	p. 143
Cellular Estimation of Distribution Algorithms	p. 146
First Theoretical Results: Takeover Times	p. 149
Computational Experiments	p. 149
Conclusions	p. 152
Software for cGAs: The JCell Framework	p. 153
The JCell Framework	p. 153
Using JCell	p. 158
Conclusions	p. 163
Applications of cGAs
Continuous Optimization	p. 167
Introduction	p. 167
Experimentation	p. 168
Tuning the Algorithm	p. 169
Comparison with Other Algorithms	p. 171
Conclusions	p. 174
Logistics: The Vehicle Routing Problem	p. 175
The Vehicle Routing Problem	p. 177
Proposed Algorithms	p. 178
Problem Representation	p. 179
Recombination	p. 180
Mutation	p. 181
Local Search	p. 182
Solving CVRP with JCell2o1i	p. 184
New Solutions to CVRP	p. 185
Conclusions	p. 186
Telecommunications: Optimization of the Broadcasting Process in MANETs	p. 187
The Problem	p. 188
Metropolitan Mobile Ad Hoc Networks. The Madhoc Simulator	p. 188
Delayed Flooding with Cumulative Neighborhood	p. 191
MOPs Definition	p. 192
A Multi-objective cGA: cMOGA	p. 193
Dealing with Constraints	p. 194
Experiments	p. 194
Parameterization of cMOGA	p. 195
Madhoc Configuration	p. 196
Results for DFCNT	p. 198
Comparing cMOGA Against NSGA-II	p. 200
Parameterization of NSGA-II	p. 200
Discussion	p. 201
Conclusions	p. 202
Bioinformatics: The DNA Fragment Assembly Problem	p. 203
The DNA Fragment Assembly Problem	p. 204
A cMA for DNA Fragment Assembly Problem	p. 206
Results	p. 208
Conclusions	p. 210
Appendix
Definition of the Benchmark Problems	p. 213
Combinatorial Optimization Problems	p. 213
COUNTSAT Problem	p. 213
Error Correcting Codes Design Problem - ECC	p. 214
Frequency Modulation Sounds - FMS	p. 215
IsoPeak Problem	p. 215
Maximum Cut of a Graph - MAXCUT	p. 216
Massively Multimodal Deceptive Problem - MMDP	p. 216
Minimum Tardy Task Problem - MTTP	p. 217
OneMax Problem	p. 218
Plateau Problem	p. 218
P-PEAKS Problem	p. 218
Satisfiability Problem - SAT	p. 219
Continuous Optimization Problems	p. 220
Academic Problems	p. 220
Real World Problems	p. 222
Multi-objective Optimization Problems	p. 223
References	p. 225
Index	p. 243
Table of Contents provided by Ingram. All Rights Reserved.

Cellular Genetic Algorithms

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