{"product_id":"evolutionary-algorithms-hardback-9781848218048","title":"Evolutionary Algorithms (Hardback) 9781848218048","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eEvolutionary Algorithms\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eAlain Petrowski (Author), Sana Ben-Hamida (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781848218048, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 31 March 2017\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e256 pages\u003cbr\u003e23.9 x 15.5 x 2 cm, 0.363 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cem\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eIn general, Petrowski and Ben-Hamid display an in-depth understanding of several optimization classes and their corresponding evolutionary algorithms, along with an impressive ability to explain, illustrate, motivate, classify and codify. Although nobody can “do it all” in a field as deep and wide as evolutionary computation, they have chosen a pertinent subset and done a fine job with it. My own copy of “Evolutionary Algorithms” became an instant go-to reference as I prepare for another semester of teaching.\u003cbr\u003e(\u003ci\u003eGenetic Programming and Evolvable Machines, December 2018)\u003c\/i\u003e\u003c\/p\u003e\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eEvolutionary algorithms are bio-inspired algorithms based on Darwin’s theory of evolution. They are expected to provide non-optimal but good quality solutions to problems whose resolution is impracticable by exact methods.\u003c\/p\u003e \u003cp\u003eIn six chapters, this book presents the essential knowledge required to efficiently implement evolutionary algorithms.\u003c\/p\u003e \u003cp\u003eChapter 1 describes a generic evolutionary algorithm as well as the basic operators that compose it. Chapter 2 is devoted to the solving of continuous optimization problems, without constraint. Three leading approaches are described and compared on a set of test functions. Chapter 3 considers continuous optimization problems with constraints. Various approaches suitable for evolutionary methods are presented. Chapter 4 is related to combinatorial optimization. It provides a catalog of variation operators to deal with order-based problems. Chapter 5 introduces the basic notions required to understand the issue of multi-objective optimization and a variety of approaches for its application. Finally, Chapter 6 describes different approaches of genetic programming able to evolve computer programs in the context of machine learning.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xi\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 Evolutionary Algorithms 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 From natural evolution to engineering 1\u003c\/p\u003e \u003cp\u003e1.2 A generic evolutionary algorithm 3\u003c\/p\u003e \u003cp\u003e1.3 Selection operators 5\u003c\/p\u003e \u003cp\u003e1.3.1 Selection pressure 5\u003c\/p\u003e \u003cp\u003e1.3.2 Genetic drift 7\u003c\/p\u003e \u003cp\u003e1.3.3 Proportional selection 9\u003c\/p\u003e \u003cp\u003e1.3.4 Tournament selection 16\u003c\/p\u003e \u003cp\u003e1.3.5 Truncation selection 18\u003c\/p\u003e \u003cp\u003e1.3.6 Environmental selection 18\u003c\/p\u003e \u003cp\u003e1.3.7 Selection operators: conclusion 20\u003c\/p\u003e \u003cp\u003e1.4 Variation operators and representation 21\u003c\/p\u003e \u003cp\u003e1.4.1 Generalities about the variation operators 21\u003c\/p\u003e \u003cp\u003e1.4.2 Crossover 22\u003c\/p\u003e \u003cp\u003e1.4.3 Mutation 25\u003c\/p\u003e \u003cp\u003e1.5 Binary representation 25\u003c\/p\u003e \u003cp\u003e1.5.1 Crossover 26\u003c\/p\u003e \u003cp\u003e1.5.2 Mutation 28\u003c\/p\u003e \u003cp\u003e1.6 The simple genetic algorithm 30\u003c\/p\u003e \u003cp\u003e1.7 Conclusion 31\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 Continuous Optimization 33\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 33\u003c\/p\u003e \u003cp\u003e2.2 Real representation and variation operators for evolutionary algorithms 35\u003c\/p\u003e \u003cp\u003e2.2.1 Crossover 36\u003c\/p\u003e \u003cp\u003e2.2.2 Mutation 40\u003c\/p\u003e \u003cp\u003e2.3 Covariance Matrix Adaptation Evolution Strategy 46\u003c\/p\u003e \u003cp\u003e2.3.1 Method presentation 46\u003c\/p\u003e \u003cp\u003e2.3.2 The CMA-ES algorithm 52\u003c\/p\u003e \u003cp\u003e2.4 A restart CMA Evolution Strategy 55\u003c\/p\u003e \u003cp\u003e2.5 Differential Evolution (DE) 57\u003c\/p\u003e \u003cp\u003e2.5.1 Initializing the population 58\u003c\/p\u003e \u003cp\u003e2.5.2 The mutation operator 58\u003c\/p\u003e \u003cp\u003e2.5.3 The crossover operator 60\u003c\/p\u003e \u003cp\u003e2.5.4 The selection operator 64\u003c\/p\u003e \u003cp\u003e2.6 Success-History based Adaptive Differential Evolution (SHADE) 65\u003c\/p\u003e \u003cp\u003e2.6.1 The algorithm 65\u003c\/p\u003e \u003cp\u003e2.6.2 Current-to-pbest\/1 mutation 67\u003c\/p\u003e \u003cp\u003e2.6.3 The success history 68\u003c\/p\u003e \u003cp\u003e2.7 Particle Swarm Optimization 70\u003c\/p\u003e \u003cp\u003e2.7.1 Standard Particle Swarm Algorithm 2007 72\u003c\/p\u003e \u003cp\u003e2.7.2 The parameters 75\u003c\/p\u003e \u003cp\u003e2.7.3 Neighborhoods 75\u003c\/p\u003e \u003cp\u003e2.7.4 Swarm initialization 76\u003c\/p\u003e \u003cp\u003e2.8 Experiments and performance comparisons 77\u003c\/p\u003e \u003cp\u003e2.8.1 Experiments 77\u003c\/p\u003e \u003cp\u003e2.8.2 Results 81\u003c\/p\u003e \u003cp\u003e2.8.3 Discussion 85\u003c\/p\u003e \u003cp\u003e2.9 Conclusion 88\u003c\/p\u003e \u003cp\u003e2.10 Appendix: set of basic objective functions used for the experiments 89\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 Constrained Continuous Evolutionary Optimization 93\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 93\u003c\/p\u003e \u003cp\u003e3.1.1 The problem with Constrained Evolutionary Optimization 93\u003c\/p\u003e \u003cp\u003e3.1.2 Taxonomy 95\u003c\/p\u003e \u003cp\u003e3.2 Penalization 98\u003c\/p\u003e \u003cp\u003e3.2.1 Static penalties 101\u003c\/p\u003e \u003cp\u003e3.2.2 Dynamic penalties 103\u003c\/p\u003e \u003cp\u003e3.2.3 Adaptive penalties 104\u003c\/p\u003e \u003cp\u003e3.2.4 Self-adaptive penalties 108\u003c\/p\u003e \u003cp\u003e3.2.5 Stochastic ranking 111\u003c\/p\u003e \u003cp\u003e3.3 Superiority of feasible solutions 112\u003c\/p\u003e \u003cp\u003e3.3.1 Special penalization 113\u003c\/p\u003e \u003cp\u003e3.3.2 Feasibility rules 114\u003c\/p\u003e \u003cp\u003e3.4 Evolving on the feasible region 117\u003c\/p\u003e \u003cp\u003e3.4.1 Searching for feasible solutions 117\u003c\/p\u003e \u003cp\u003e3.4.2 Maintaining feasibility using special operators 120\u003c\/p\u003e \u003cp\u003e3.5 Multi-objective methods 123\u003c\/p\u003e \u003cp\u003e3.5.1 Bi-objective techniques 124\u003c\/p\u003e \u003cp\u003e3.5.2 Multi-objective techniques 128\u003c\/p\u003e \u003cp\u003e3.6 Parallel population approaches 130\u003c\/p\u003e \u003cp\u003e3.7 Hybrid methods 132\u003c\/p\u003e \u003cp\u003e3.8 Conclusion 132\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Combinatorial Optimization 135\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 135\u003c\/p\u003e \u003cp\u003e4.1.1 Solution encoding 136\u003c\/p\u003e \u003cp\u003e4.1.2 The knapsack problem (KP) 137\u003c\/p\u003e \u003cp\u003e4.1.3 The Traveling Salesman Problem (TSP) 139\u003c\/p\u003e \u003cp\u003e4.2 The binary representation and variation operators 140\u003c\/p\u003e \u003cp\u003e4.2.1 Binary representation for the 0\/1-KP 142\u003c\/p\u003e \u003cp\u003e4.2.2 Binary representation for the TSP 142\u003c\/p\u003e \u003cp\u003e4.3 Order-based Representation and variation operators 143\u003c\/p\u003e \u003cp\u003e4.3.1 Crossover operators 143\u003c\/p\u003e \u003cp\u003e4.3.2 Mutation operators 155\u003c\/p\u003e \u003cp\u003e4.3.3 Specific operators 158\u003c\/p\u003e \u003cp\u003e4.3.4 Discussion 159\u003c\/p\u003e \u003cp\u003e4.4 Conclusion 163\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Multi-objective Optimization 165\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 165\u003c\/p\u003e \u003cp\u003e5.2 Problem formalization 166\u003c\/p\u003e \u003cp\u003e5.2.1 Pareto dominance 166\u003c\/p\u003e \u003cp\u003e5.2.2 Pareto optimum 167\u003c\/p\u003e \u003cp\u003e5.2.3 Multi-objective optimization algorithms 167\u003c\/p\u003e \u003cp\u003e5.3 The quality indicators 167\u003c\/p\u003e \u003cp\u003e5.3.1 The measure of the hypervolume or “S-metric” 168\u003c\/p\u003e \u003cp\u003e5.4 Multi-objective evolutionary algorithms 169\u003c\/p\u003e \u003cp\u003e5.5 Methods using a “Pareto ranking” 169\u003c\/p\u003e \u003cp\u003e5.5.1 Nsga-ii 171\u003c\/p\u003e \u003cp\u003e5.6 Many-objective problems 176\u003c\/p\u003e \u003cp\u003e5.6.1 The relaxed dominance approaches 177\u003c\/p\u003e \u003cp\u003e5.6.2 Aggregation-based approaches 177\u003c\/p\u003e \u003cp\u003e5.6.3 Indicator-based approaches 180\u003c\/p\u003e \u003cp\u003e5.6.4 Diversity-based approaches 180\u003c\/p\u003e \u003cp\u003e5.6.5 Reference set approaches 180\u003c\/p\u003e \u003cp\u003e5.6.6 Preference-based approaches 181\u003c\/p\u003e \u003cp\u003e5.7 Conclusion 181\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Genetic Programming for Machine Learning 183\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 183\u003c\/p\u003e \u003cp\u003e6.2 Syntax tree representation 186\u003c\/p\u003e \u003cp\u003e6.2.1 Closure and sufficiency 187\u003c\/p\u003e \u003cp\u003e6.2.2 Bloat control 187\u003c\/p\u003e \u003cp\u003e6.3 Evolving the syntax trees 187\u003c\/p\u003e \u003cp\u003e6.3.1 Initializing the population 188\u003c\/p\u003e \u003cp\u003e6.3.2 Crossover 188\u003c\/p\u003e \u003cp\u003e6.3.3 Mutations 191\u003c\/p\u003e \u003cp\u003e6.3.4 Advanced tree-based GP 193\u003c\/p\u003e \u003cp\u003e6.4 GP in action: an introductory example 194\u003c\/p\u003e \u003cp\u003e6.4.1 The symbolic regression 195\u003c\/p\u003e \u003cp\u003e6.4.2 First example 196\u003c\/p\u003e \u003cp\u003e6.5 Alternative Genetic Programming Representations 200\u003c\/p\u003e \u003cp\u003e6.5.1 Linear-based GP Representation 201\u003c\/p\u003e \u003cp\u003e6.5.2 Graph-based Genetic Programming Representation 206\u003c\/p\u003e \u003cp\u003e6.6 Example of application: intrusion detection in a computer system 210\u003c\/p\u003e \u003cp\u003e6.6.1 Learning data 211\u003c\/p\u003e \u003cp\u003e6.6.2 GP design 212\u003c\/p\u003e \u003cp\u003e6.6.3 Results 214\u003c\/p\u003e \u003cp\u003e6.7 Conclusion 215\u003c\/p\u003e \u003cp\u003eBibliography 217\u003c\/p\u003e \u003cp\u003eIndex 233\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Computer science [\u003ca title=\"See our other books on Computer science\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Computer%20science%20%5BUY%5D%22\"\u003eUY\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-ISTE","offers":[{"title":"Brand 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