Genetic Algorithms + Data Structures = Evolution ProgramsGenetic algorithms are founded upon the principle of evolution, i.e., survival of the fittest. Hence evolution programming techniques, based on genetic algorithms, are applicable to many hard optimization problems, such as optimization of functions with linear and nonlinear constraints, the traveling salesman problem, and problems of scheduling, partitioning, and control. The importance of these techniques is still growing, since evolution programs are parallel in nature, and parallelism is one of the most promising directions in computer science. The book is selfcontained and the only prerequisite is basic undergraduate mathematics. This third edition has been substantially revised and extended by three new chapters and by additional appendices containing working material to cover recent developments and a change in the perception of evolutionary computation. 
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excellent book ,provides all that are need to understand the GAs effectively
Contents
Introduction  1 
Genetic Algorithms  11 
GAs What Are They?  13 
11 Optimization of a simple function  18 
111 Representation  19 
112 Initial population  20 
114 Genetic operators  21 
116 Experimental results  22 
731 Five test cases  144 
732 Experiments  147 
74 Other possibilities  150 
75 GENOCOP III  154 
Evolution Strategies and Other Methods  159 
81 Evolution of evolution strategies  160 
82 Comparison of evolution strategies and genetic algorithms  164 
83 Multimodal and multiobjective function optimization  168 
121 Representing a strategy  23 
123 Experimental results  24 
13 Traveling salesman problem  25 
14 Hill climbing simulated annealing and genetic algorithms  26 
15 Conclusions  30 
GAs How Do They Work?  33 
GAs Why Do They Work?  45 
GAs Selected Topics  57 
41 Sampling mechanism  58 
42 Characteristics of the function  65 
43 Contractive mapping genetic algorithms  68 
44 Genetic algorithms with varying population size  72 
45 Genetic algorithms constraints and the knapsack problem  80 
451 The 01 knapsack problem and the test data  81 
452 Description of the algorithms  82 
453 Experiments and results  84 
46 Other ideas  88 
Numerical Optimization  95 
Binary or Float?  97 
51 The test case  100 
522 The floating point implementation  101 
532 Nonuniform mutation  103 
533 Other operators  104 
54 Time performance  105 
6 Fine Local Tuning  107 
61 The test cases  108 
611 The linearquadratic problem  109 
613 The pushcart problem  110 
621 The representation  111 
63 Experiments and results  113 
64 Evolution program versus other methods  114 
642 The harvest problem  115 
644 The significance of nonuniform mutation  117 
65 Conclusions  118 
Handling Constraints  121 
the GENOCOP system  122 
711 An example  125 
712 Operators  127 
713 Testing GENOCOP  130 
GENOCOP II  134 
73 Other techniques  141 
832 Multiobjective optimization  171 
84 Other evolution programs  172 
Evolution Programs  179 
The Transportation Problem  181 
911 Classical genetic algorithms  183 
912 Incorporating problemspecific knowledge  185 
913 A matrix as a representation structure  188 
914 Conclusions  194 
92 The nonlinear transportation problem  196 
925 Parameters  198 
927 Experiments and results  201 
928 Conclusions  206 
The Traveling Salesman Problem  209 
Evolution Programs for Various Discrete Problems  239 
112 The timetable problem  246 
113 Partitioning objects and graphs  247 
114 Path planning in a mobile robot environment  253 
115 Remarks  261 
12 Machine Learning  267 
121 The Michigan approach  270 
122 The Pitt approach  274 
the GIL system  276 
1232 Genetic operators  277 
124 Comparison  280 
125 REGAL  281 
Evolutionary Programming and Genetic Programming  283 
132 Genetic programming  285 
A Hierarchy of Evolution Programs  289 
Evolution Programs and Heuristics  307 
a summary  309 
152 Feasible and infeasible solutions  312 
153 Heuristics for evaluating individuals  314 
Conclusions  329 
Appendix A  337 
Appendix B  349 
Appendix C  353 
Appendix D  359 
363  
383  
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Genetic Algorithms + Data Structures = Evolution Programs Professor of Computer Science Zbigniew Michalewicz No preview available  2014 
Common terms and phrases
applied approach average best individual binary string bits Chapter chromosome chromosome representation contractive mapping convergence cost crossover crossover operator data structures decoders defined discussed domain edges encoding evaluation function evolution process evolution program evolution strategies evolutionary algorithms evolutionary computation evolutionary programming evolutionary techniques example experiments feasible and infeasible feasible solution floating point GAMS GAVaPS gene genetic algorithm genetic operators GENETIC2 GENOCOP global optimum heuristic implementation incorporate infeasible individuals infeasible solutions initial population integer iteration knapsack linear constraints matrix method minimize mutation mutation operator nodes numerical optimization objective function offspring optimization problems parameters parents path penalty functions performance popsize possible potential solutions probability procedure random number randomly recombination repair algorithms represents robot runs schema schemata search space selection sequence simulated annealing single solution vector solve subtours tion tour transportation problem traveling salesman problem variables