Libreria Herrero Books :: EVOLUTIONARY COMPUTATION WITH BIOGEOGRAPHYBASED OPTIMIZATION
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EVOLUTIONARY COMPUTATION WITH BIOGEOGRAPHY–BASED OPTIMIZATION

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9781848218079 ::  EVOLUTIONARY COMPUTATION WITH BIOGEOGRAPHYBASED OPTIMIZATION
ISBN:

9781848218079

EditorialJOHN WILEY & SONS LTD.
Edicion:
Páginas:344
Idioma:INGLES
P.V.P.: 158,31 € + 4% IVA = 164,64 €
Dto 5% Ahorras 8,23 €
Importe final iva incl. 156,41 €
EN STOCK / ENVIO EN 8-10 DIAS

EVOLUTIONARY COMPUTATION ALGORITHMS ARE EMPLOYED TO MINIMIZE FUNCTIONS WITH LARGE NUMBER OF VARIABLES. BIOGEOGRAPHY–BASED OPTIMIZATION (BBO) IS AN OPTIMIZATION ALGORITHM THAT IS BASED ON THE SCIENCE OF BIOGEOGRAPHY, WHICH RESEARCHES THE MIGRATION PATTERNS OF SPECIES. THESE MIGRATION PARADIGMS PROVIDE THE MAIN LOGIC BEHIND BBO. DUE TO THE CROSS–DISCIPLINARY NATURE OF THE OPTIMIZATION PROBLEMS, THERE IS A NEED TO DEVELOP MULTIPLE APPROACHES TO TACKLE THEM AND TO STUDY THE THEORETICAL REASONING BEHIND THEIR PERFORMANCE. THIS BOOK EXPLAINS THE MATHEMATICAL MODEL OF BBO ALGORITHM AND ITS VARIANTS CREATED TO COPE WITH CONTINUOUS DOMAIN PROBLEMS (WITH AND WITHOUT CONSTRAINTS) AND COMBINATORIAL PROBLEMS.

CHAPTER 1. THE SCIENCE OF BIOGEOGRAPHY 1 1.1. INTRODUCTION  1 1.2. ISLAND BIOGEOGRAPHY  3 1.3. INFLUENCE FACTORS FOR BIOGEOGRAPHY  6 CHAPTER 2. BIOGEOGRAPHY AND BIOLOGICAL OPTIMIZATION  11 2.1. A MATHEMATICAL MODEL OF BIOGEOGRAPHY 11 2.2. BIOGEOGRAPHY AS AN OPTIMIZATION PROCESS  16 2.3. BIOLOGICAL OPTIMIZATION  19 2.3.1. GENETIC ALGORITHMS  19 2.3.2. EVOLUTION STRATEGIES  20 2.3.3. PARTICLE SWARM OPTIMIZATION 21 2.3.4. ARTIFICIAL BEE COLONY ALGORITHM 22 2.4. CONCLUSION 23 CHAPTER 3. A BASIC BBO ALGORITHM 25 3.1. BBO DEFINITIONS AND ALGORITHM  25 3.1.1. MIGRATION 26 3.1.2. MUTATION  27 3.1.3. BBO IMPLEMENTATION 27 3.2. DIFFERENCES BETWEEN BBO AND OTHER OPTIMIZATION ALGORITHMS  35 3.2.1. BBO AND GENETIC ALGORITHMS 35 3.2.2. BBO AND OTHER ALGORITHMS  36 3.3. SIMULATIONS 37 3.4. CONCLUSION 44 CHAPTER 4. BBO EXTENSIONS 45 4.1. MIGRATION CURVES  45 4.2. BLENDED MIGRATION 49 4.3. OTHER APPROACHES TO BBO 51 4.4. APPLICATIONS  56 4.5. CONCLUSION 59 CHAPTER 5. BBO AS A MARKOV PROCESS 61 5.1. MARKOV DEFINITIONS AND NOTATIONS  61 5.2. MARKOV MODEL OF BBO  72 5.3. BBO CONVERGENCE 79 5.4. MARKOV MODELS OF BBO EXTENSIONS 90 5.5. CONCLUSIONS  99 CHAPTER 6. DYNAMIC SYSTEM MODELS OF BBO 103 6.1. BASIC NOTATION 103 6.2. DYNAMIC SYSTEM MODELS OF BBO 105 6.3. APPLICATIONS TO BENCHMARK PROBLEMS  119 6.4. CONCLUSIONS  122 CHAPTER 7. STATISTICAL MECHANICS APPROXIMATIONS OF BBO  123 7.1. PRELIMINARY FOUNDATION  123 7.2. STATISTICAL MECHANICS MODEL OF BBO 128 7.2.1. MIGRATION 128 7.2.2. MUTATION  134 7.3. FURTHER DISCUSSION 141 7.3.1. FINITE POPULATION EFFECTS 141 7.3.2. SEPARABLE FITNESS FUNCTIONS  142 7.4. CONCLUSIONS  143 CHAPTER 8. BBO FOR COMBINATORIAL OPTIMIZATION  145 8.1. TRAVELING SALESMAN PROBLEM 147 8.2. BBO FOR THE TSP  148 8.2.1. POPULATION INITIALIZATION 148 8.2.2. MIGRATION IN THE TSP 150 8.2.3. MUTATION IN THE TSP 157 8.2.4. IMPLEMENTATION FRAMEWORK 159 8.3. GRAPH COLORING 163 8.4. KNAPSACK PROBLEM 165 8.5. CONCLUSION 167 CHAPTER 9. CONSTRAINED BBO  169 9.1. CONSTRAINED OPTIMIZATION 170 9.2. CONSTRAINT–HANDLING METHODS 172 9.2.1. STATIC PENALTY METHODS  172 9.2.2. SUPERIORITY OF FEASIBLE POINTS  173 9.2.3. THE ECLECTIC EVOLUTIONARY ALGORITHM 174 9.2.4. DYNAMIC PENALTY METHODS  174 9.2.5. ADAPTIVE PENALTY METHODS  176 9.2.6. THE NICHED–PENALTY APPROACH  177 9.2.7. STOCHASTIC RANKING  178 9.2.8. –LEVEL COMPARISONS  178 9.3. BBO FOR CONSTRAINED OPTIMIZATION  179 9.4. CONCLUSION 185 CHAPTER 10. BBO IN NOISY ENVIRONMENTS 187 10.1. NOISY FITNESS FUNCTIONS  188 10.2. INFLUENCE OF NOISE ON BBO 190 10.3. BBO WITH RE–SAMPLING  193 10.4. THE KALMAN BBO  196 10.5. EXPERIMENTAL RESULTS 199 10.6. CONCLUSION  201 CHAPTER 11. MULTI–OBJECTIVE BBO  203 11.1. MULTI–OBJECTIVE OPTIMIZATION PROBLEMS 204 11.2. MULTI–OBJECTIVE BBO 211 11.2.1. VECTOR EVALUATED BBO 211 11.2.2. NON–DOMINATED SORTING BBO  213 11.2.3. NICHED PARETO BBO 216 11.2.4. STRENGTH PARETO BBO  218 11.3. REAL–WORLD APPLICATIONS 223 11.3.1. WAREHOUSE SCHEDULING MODEL 223 11.3.2. OPTIMIZATION OF WAREHOUSE SCHEDULING  229 11.4. CONCLUSION  231 CHAPTER 12. HYBRID BBO ALGORITHMS  233 12.1. OPPOSITION–BASED BBO 234 12.1.1. OPPOSITION DEFINITIONS AND CONCEPTS  234 12.1.2. OPPOSITIONAL BBO  236 12.1.3. EXPERIMENTAL RESULTS 238 12.2. BBO WITH LOCAL SEARCH  240 12.2.1. LOCAL SEARCH METHODS  240 12.2.2. SIMULATION RESULTS  245 12.3. BBO WITH OTHER EAS 247 12.3.1. ITERATION–LEVEL HYBRIDIZATION  247 12.3.2. ALGORITHM–LEVEL HYBRIDIZATION 250 12.3.3. EXPERIMENTAL RESULTS 254 12.4. CONCLUSION  256 APPENDICES 259 APPENDIX A. UNCONSTRAINED BENCHMARK FUNCTIONS  261 APPENDIX B. CONSTRAINED BENCHMARK FUNCTIONS  265 APPENDIX C. MULTI–OBJECTIVE BENCHMARK FUNCTIONS  289 BIBLIOGRAPHY 309 INDEX 325

BIOLOGIA
INGENIERIA / TECNOLOGIA
INGENIERIA ELECTRONICA
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