Klienditugi: 7440010 (E-R 10-18)

E-raamat: Evolutionary Computation [Taylor & Francis e-raamat]

Lakhmi C. Jain (University of South Australia, Adelaide), Beatrice Lazzerini (Universita' di Pisa, Italy), D. Dumitrescu (University of Cluj-Napoca, Romania), A. Dumitrescu (University of Savoie, Avignon, France)

Formaat: 420 pages
Sari: International Series on Computational Intelligence
Ilmumisaeg: 22-Jun-2000
Kirjastus: CRC Press Inc
ISBN-13: 9780429175411

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Formaat: 420 pages
Sari: International Series on Computational Intelligence
Ilmumisaeg: 22-Jun-2000
Kirjastus: CRC Press Inc
ISBN-13: 9780429175411

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Rohkem infot Taylor & Francis e-raamatute kohta

Raamatu kodulehekülg: https://www.taylorfrancis.com/books/9780429175411

A presentation of the main ideas, models, and algorithms of a field of artificial intelligence known as evolutionary computation. The method uses a metaphor of biological reproduction and selection to evolve a population of candidate (potential) solutions to a given problem. Coverage includes binary and real-valued encoding, including selection, crossover, and mutation, as well as applications such as decision strategy selection, training and design of neural networks, approaches to pattern recognition, and cellular automata. Annotation c. Book News, Inc., Portland, OR (booknews.com)

Rapid advances in evolutionary computation have opened up a world of applications-a world rapidly growing and evolving. Decision making, neural networks, pattern recognition, complex optimization/search tasks, scheduling, control, automated programming, and cellular automata applications all rely on evolutionary computation.

Evolutionary Computation presents the basic principles of evolutionary computing: genetic algorithms, evolution strategies, evolutionary programming, genetic programming, learning classifier systems, population models, and applications. It includes detailed coverage of binary and real encoding, including selection, crossover, and mutation, and discusses the (m+l) and (m,l) evolution strategy principles. The focus then shifts to applications: decision strategy selection, training and design of neural networks, several approaches to pattern recognition, cellular automata, applications of genetic programming, and more.

Principles of evolutionary computation

(21)

Introduction

(1)

Genes and chromosomes

(3)

Gene structure and DNA transcription

(1)

Gene expression as phenotypic traits

(2)

Diploid and haploid genotypes

(1)

Early EC research

(2)

Basic evolutionary computation models

(2)

Genetic algorithms

(1)

Evolutionary programming

(1)

Evolution strategies

(1)

Other EC approaches

(2)

Genetic programming

(1)

Learning classifier systems

(1)

Michigan approach

(1)

Pittsburgh approach

(1)

Structure of an evolutionary algorithm

(5)

Encoding solutions

(2)

Selection and search operators

(1)

Fitness function

(1)

Recombination operator

(1)

Mutation operator

(1)

Selection

(1)

Innovative vs. conservative operators

(1)

Components of an EC algorithm

(1)

Basic evolutionary algorithm

(5)

Termination condition

(1)

Result of an evolutionary algorithm

(1)

References and bibliography

(3)

Genetic algorithms

(18)

Introduction

(2)

Problem representation and fitness function

(2)

Representation

(1)

Fitness function

(1)

Chromosome evaluation

(1)

Implicit fitness and coevolution

(1)

Fitness landscape

(1)

Search progress

(1)

Basic elements of genetic algorithms

(2)

Canonical genetic algorithm

(4)

Representation

(1)

Simple genetic algorithm

(2)

Replacement strategies

(1)

Initial population

(1)

Partially enumerative initialization

(1)

Doping

(1)

Schemata and building blocks

(7)

Notions concerning schemata

(3)

Building block hypothesis and schema theorem

(1)

Implicit parallelism

(1)

Genetic drift

(1)

References and bibliography

(2)

Basic selection schemes in evolutionary algorithms

(18)

Introduction

(1)

Selection purposes

(2)

Mating pool

(1)

Selection for recombination and selection for replacement

(1)

Fitness function

(2)

Fitness and scaling

(1)

Implicit fitness evaluation

(1)

Coevolutionary fitness evaluation

(1)

Selection pressure and takeover time

(2)

Selection pressure

(1)

Takeover time

(1)

Selection pressure and search progress

(1)

Proportional selection

(8)

Selection probability

(1)

Basic definitions

(1)

Average number of copies

(1)

Proportional selection algorithm

(2)

Premature and slow convergence

(1)

Premature convergence

(1)

Slow convergence

(1)

Takeover time for proportional selection

(1)

Variants of proportional selection

(1)

Stochastic sampling with replacement

(1)

Sampling rate assignment

(1)

Stochastic universal sampling

(1)

Truncation

(3)

References and bibliography

(3)

Selection based on scaling and ranking mechanisms

(26)

Introduction

(1)

Scale transformation

(1)

Static scaling mechanisms

(2)

Linear scaling

(1)

Power law scaling

(1)

Logarithmic scaling

(1)

Dynamic scaling

(2)

Sigma truncation

(1)

Window scaling

(1)

Noisy fitness functions

(1)

Fitness remapping for minimization problems

(1)

Rank-based selection

(10)

Linear ranking selection

(1)

Selection probability for linear ranking

(1)

Range of parameter Max

(2)

Another expression of selection probability

(1)

Selection probabilities for the best and worst individuals

(1)

Selection pressure and takeover time for linear ranking

(1)

An example

(1)

Selection pressure and population diversity

(1)

Nonlinear ranking

(1)

Exponential ranking

(1)

Geometric distribution ranking

(1)

Biased exponential ranking

(1)

General nonlinear ranking

(1)

Binary tournament

(3)

Deterministic tournament

(1)

Probabilistic tournament

(1)

Boltzmann tournament

(1)

q-tournament selection

(5)

Score-based tournament

(1)

Local tournament

(1)

Concluding remarks on tournament selection

(1)

References and bibliography

(3)

Further selection strategies

(20)

Introduction

(1)

Classification of selection strategies

(2)

Elitist strategies

(1)

Generation gap methods

(2)

Overlapping and non-overlapping models

(1)

Generation gap

(1)

Steady-state evolutionary algorithms

(2)

Basic steady-state model

(1)

Generalized steady-state algorithm

(1)

Generational elitist strategies in GAs

(1)

Michalewicz selection

(1)

Boltzmann selection

(3)

Boltzmann selection by scaling

(2)

Simulated annealing

(1)

PRSA method

(1)

Other selection methods

(2)

Greedy over-selection

(1)

Coevolutionary selection models

(1)

Genetic drift

(5)

References and bibliography

(4)

Recombination operators within binary encoding

103

(28)

Introduction

103

(1)

One-point crossover

104

(3)

Basic crossover operator

104

(2)

Formal definition of crossover operator

106

(1)

Two-point crossover

107

(1)

N-point crossover

108

(2)

Punctuated crossover

110

(2)

Segmented crossover

112

(1)

Shuffle crossover

113

(1)

Uniform crossover

114

(1)

Basic method

114

(1)

Generalizations

115

(1)

Other crossover operators and some comparisons

115

(3)

Multi-parent and one-descendent operators

116

(1)

Reduced surrogate

116

(1)

RRR operator

116

(1)

Experimental and theoretical studies

117

(1)

Comparative studies

117

(1)

Positional bias and distributional bias

117

(1)

Crossover probability

118

(2)

Setting crossover probability

120

(1)

Mating

120

(1)

N-point crossover algorithm revisited

121

(2)

Selection for survival or replacement

123

(1)

General remarks about crossover within the framework of binary encoding

124

(7)

References and bibliography

125

(6)

Mutation operators and related topics

131

(22)

Introduction

131

(2)

Mutation with binary encoding

133

(2)

Mutation rate

134

(1)

Mutation rate values

134

(1)

Strong and weak mutation operators

135

(4)

Selecting a position for mutation

136

(1)

Strong mutation operator

136

(2)

Weak mutation operator

138

(1)

Mutation within a unique chromosome

139

(1)

Non-uniform mutation

139

(3)

Time-dependent mutation rate

139

(2)

Fitness-dependent mutation rate

141

(1)

Adaptive non-uniform mutation

142

(1)

Self-adaptation of mutation rate

142

(3)

Self-adaptation mechanism

143

(1)

Mutation rate modification

143

(1)

Transformed genotype

144

(1)

Local mutation probabilities

144

(1)

Crossover vs. mutation

145

(1)

The inversion operator

146

(1)

Selection vs. variation operators

147

(1)

Simple genetic algorithm revisited

148

(5)

References and bibliography

150

(3)

Schema theorem, building blocks, and related topics

153

(34)

Introduction

153

(2)

Elements characterizing schemata

155

(2)

Schema dynamics

157

(1)

Effect of selection on schema dynamics

158

(5)

Schema dynamics within selection

158

(3)

Dynamics of above/below-average schema

161

(2)

Effect of recombination on schema dynamics

163

(3)

Schema disruption probability

163

(2)

Actual disruption probability

165

(1)

Survival probability

166

(1)

Combined effect of selection and recombination on schema dynamics

166

(4)

Schema dynamics within selection and crossover

167

(2)

Qualitative results concerning schema dynamics

169

(1)

Effect of mutation on schema dynamics

170

(3)

Schema theorem

173

(3)

Schema dynamics within selection and search operators

173

(1)

Approximating schema dynamics

174

(1)

Fundamental theorem

175

(1)

Building block

176

(1)

Building block hypothesis and linkage problem

177

(3)

Schema linkage

178

(1)

Concluding remarks

179

(1)

Generalizations of schema theorem

180

(1)

Deceptive functions

181

(6)

References and bibliography

184

(3)

Real-valued encoding

187

(26)

Introduction

187

(1)

Real-valued vectors

188

(1)

Recombination operators for real-valued encoding

189

(10)

Discrete recombination

190

(1)

Continuous recombination

191

(1)

Complete continuous recombination

192

(1)

Convex (intermediate) recombination

192

(1)

One offspring

192

(1)

Two offspring

193

(1)

Random coefficient

193

(1)

Local crossover

194

(1)

SBX operator

195

(1)

Multiple-parent recombination

196

(1)

Fitness-based recombination

197

(1)

Fitness-based scan

197

(1)

Heuristic crossover

198

(1)

Simplex crossover

198

(1)

Random simplex crossover

199

(1)

Mutation operators for real-valued encoding

199

(10)

Uniform mutation

199

(1)

One-position mutation operator

200

(1)

All-positions mutation operator

201

(1)

Non-uniform mutation

202

(1)

A non-uniform mutation operator

202

(2)

Generalized non-uniform mutation

204

(2)

Normal perturbation-induced mutation

206

(1)

Multiplicative self-adaptation procedure

207

(1)

Additive self-adaptation procedures

207

(1)

Other self-adaptation procedures

208

(1)

Cauchy perturbation

208

(1)

Concluding remarks

209

(4)

References and bibliography

211

(2)

Hybridization, parameter setting, and adaptation

213

(18)

Introduction

213

(1)

Specialized representation and hybridization within GAs

214

(4)

Specific representation

214

(1)

Hybridization

215

(1)

Use of specific encoding and hybridization

216

(2)

Parameter setting and adaptive GAs

218

(5)

Parameter setting in GAs

218

(1)

Parameter setting and representation adaptation

219

(2)

Adaptive fitness of a search operator

221

(2)

Adaptive GAs

223

(8)

Adaptation problem

223

(2)

Adaptive techniques based on fuzzy logic control

225

(1)

References and bibliography

225

(6)

Adaptive representations: messy genetic algorithms, delta coding, and diploidic representation

231

(30)

Introduction

231

(2)

Principles of messy genetic algorithms

233

(6)

Variable-length encoding

233

(1)

Linkage problem

234

(1)

Linkage within binary encoding

234

(1)

Solutions to the linkage problem

235

(1)

Messy encoding

236

(1)

Incompleteness and ambiguity

237

(1)

Dealing with over-specification

238

(1)

Dealing with under-specification

238

(1)

Recombination within messy genetic operators

239

(3)

Recombination

239

(1)

Examples

240

(2)

Mutation

242

(1)

Computational models

243

(1)

Generalizations of messy GAs

244

(1)

Other adaptive representation approaches

245

(2)

ARGOT system

246

(1)

Dynamic parameter encoding

246

(1)

Delta coding

247

(5)

Real-valued delta coding

247

(1)

Real-valued delta coding procedure

248

(2)

The algorithm

250

(2)

Diploidy and dominance

252

(9)

Haploid and diploid chromosome structures revisited

252

(1)

Dominance

253

(1)

Diploidic representation

254

(1)

Triallelic representation

254

(2)

Quadrallelic representation

256

(1)

Evolving dominance map

256

(1)

Use of diploidy

257

(1)

References and bibliography

257

(4)

Evolution strategies and evolutionary programming

261

(22)

Introduction

261

(1)

Evolution strategies

261

(2)

(1+1) strategy

263

(5)

1/5 success rule

264

(1)

Standard deviation adaptation

265

(1)

Schwefel's version of the 1/5 success rule

266

(2)

Multimembered evolution strategies

268

(2)

Representation of individuals

269

(1)

Standard mutation

270

(2)

Standard mutation of the control parameters

270

(2)

Genotypes including covariance matrix. Correlated mutation

272

(2)

Covariance matrix for mutation

272

(1)

Correlated mutations

273

(1)

Cauchy perturbations

274

(1)

Cauchy distribution

274

(1)

Cauchy perturbation-induced mutation

274

(1)

Evolutionary programming

275

(4)

Sequential machine model

276

(2)

Function optimization by evolutionary programming

278

(1)

Evolutionary programming using Cauchy perturbation

279

(4)

References and bibliography

279

(4)

Population models and parallel implementations

283

(16)

Introduction

283

(1)

Niching methods

284

(1)

Fitness sharing

284

(3)

Sharing function

286

(1)

Niche count

286

(1)

Shared fitness

286

(1)

Crowding

287

(1)

Island and stepping stone models

287

(2)

Fine-grained and diffusion models

289

(1)

Coevolution

290

(1)

Competitive models

290

(1)

Cooperative models

290

(1)

Baldwin effect

291

(1)

Parallel implementation of evolutionary algorithms

292

(7)

Subpopulations with migration

292

(1)

Island model

293

(1)

Coarse-grained models

293

(1)

Fine-grained models

293

(1)

Diffusion model

294

(1)

Overlapping subpopulations without migration

294

(1)

References and bibliography

294

(5)

Genetic programming

299

(24)

Introduction

299

(1)

Early GP approaches

300

(1)

Program-generating language

301

(2)

Terminal and function sets

301

(2)

Closure property

303

(1)

Problem language and implementation language

303

(1)

GP program structures

303

(2)

Tree structures

304

(1)

Graph structures

304

(1)

Linear structures

305

(1)

Initialization of tree structures

305

(2)

Grow method

306

(1)

Full method

306

(1)

Ramped half-and-half method

306

(1)

Fitness calculation

307

(2)

Recombination operators

309

(4)

Standard recombination operator

309

(1)

Brood recombination

310

(1)

Selecting crossover points

311

(1)

Introns

312

(1)

Mutation

313

(2)

Mutation of tree-structured programs

314

(1)

Macromutation

314

(1)

Micromutation

314

(1)

Mutation of linearly represented programs

315

(1)

Mutation strategies

315

(1)

Selection

315

(1)

Selection for recombination

316

(1)

Selection for replacement

316

(1)

Population models

316

(1)

Parallel implementation

317

(1)

Basic GP algorithm

317

(6)

GP procedure setting

317

(1)

Generational algorithm

318

(1)

Steady-state GP algorithm

319

(1)

References and bibliography

320

(3)

Learning classifier systems

323

(20)

Introduction

323

(1)

Michigan and Pittsburgh families of learning classifier systems

324

(2)

Michigan approach

324

(1)

Pittsburgh approach

325

(1)

Michigan classifier systems

326

(2)

Structure of a Holland system

326

(1)

Rules and messages

327

(1)

Bucket brigade algorithm

328

(9)

Principle of the algorithm

328

(1)

Bargaining procedure

329

(1)

Bid and winning probability

330

(2)

Updating strength of a winning classifier

332

(1)

Updating strength of a producing classifier

333

(1)

Income tax

334

(1)

Property tax

334

(1)

Updating strength for remaining situations

335

(1)

Taxing the winners. Updating strength revisited

336

(1)

Remarks on bucket brigade algorithm

336

(1)

Pittsburgh classifier systems

337

(1)

Fuzzy classifier systems

337

(6)

Fuzzy Michigan classifier systems

338

(1)

Fuzzy Pittsburgh classifier systems

338

(1)

Learning fuzzy memberships

339

(1)

Learning fuzzy rules with fixed fuzzy membership functions

339

(1)

Learning fuzzy rules and membership functions separately

339

(1)

Learning fuzzy rules and membership functions simultaneously

340

(1)

References and bibliography

340

(3)

Applications of evolutionary computation.

343

(36)

Introduction

343

(1)

General applications of evolutionary computation

344

(2)

Main application areas

346

(8)

Optimization and search applications

354

(1)

Optimization

354

(1)

355

(1)

Choosing a decision strategy

355

(2)

Neural network training and design

357

(3)

Neural network training using evolutionary computation

357

(1)

General applications

358

(1)

Evolutionary algorithms as training procedures

359

(1)

Establishing neural network architecture

359

(1)

Pattern recognition applications

360

(5)

A simple genetic algorithm for fuzzy clustering

361

(3)

Other approaches

364

(1)

Cellular automata

365

(9)

Basic notions

365

(2)

Specification of a cellular automaton

367

(2)

CA applications

369

(1)

Determining transition functions

370

(1)

CA rule detection

370

(4)

Evolutionary algorithms vs. other heuristics

374

(5)

References and bibliography

374

(5)

Index

379

Beatrice Lazzerini, D. Dumitrescu, Lakhmi C. Jain. A. Dumitrescu

Püsilink: https://www.kriso.ee/db/9780429175411_pe.html

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Evolutionary programming (Computer science)

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