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Inhaltsangabe
1. Evolutionary Algorithms: Revisited.- 1.1 Introduction.- 1.2 Stochastic Optimization Algorithms.- 1.2.1 Monte Carlo Algorithm.- 1.2.2 Hill Climbing Algorithm.- 1.2.3 Simulated Annealing Algorithm.- 1.2.4 Evolutionary Algorithms.- 1.3 Properties of Stochastic Optimization Algorithms.- 1.4 Variants of Evolutionary Algorithms.- 1.4.1 Genetic Algorithms.- 1.4.2 Evolution Strategies.- 1.4.3 Evolutionary Programming.- 1.4.4 Genetic Programming.- 1.5 Basic Mechanisms of Evolutionary Algorithms.- 1.5.1 Crossover Mechanisms.- 1.5.2 Mutation Mechanisms.- 1.5.3 Selection Mechanisms.- 1.6 Similarities and Differences of Evolutionary Algorithms.- 1.7 Merits and Demerits of Evolutionary Algorithms.- 1.7.1 Merits.- 1.7.2 Demerits.- 1.8 Summary.- 2. A Novel Evolution Strategy Algorithm.- 2.1 Introduction.- 2.2 Development of New Variation Operators.- 2.2.1 Subpopulations-Based Max-mean Arithmetical Crossover.- 2.2.2 Time-Variant Mutation.- 2.3 Proposed Novel Evolution Strategy.- 2.3.1 Initial Population.- 2.3.2 Crossover.- 2.3.3 Mutation.- 2.3.4 Evaluation.- 2.3.5 Alternation of Generation.- 2.4 Proposed NES: How Does It Work?.- 2.5 Performance of the Proposed Evolution Strategy.- 2.5.1 Test Functions.- 2.5.2 Implementation and Results.- 2.6 Empirical Investigations for Exogenous Parameters.- 2.6.1 Investigation for Optimal Subpopulation Number.- 2.6.2 Investigation for Optimal Degree of Dependency.- 2.7 Summary.- 3. Evolutionary Optimization of Constrained Problems.- 3.1 Introduction.- 3.2 Constrained Optimization Problem.- 3.3 Constraint-Handling in Evolutionary Algorithms.- 3.4 Characteristics of the NES Algorithm.- 3.4.1 Characteristics of the SBMAC Operator.- 3.4.2 Characteristics of the TVM Operator.- 3.4.3 Effects of the Elitist Selection.- 3.5 Construction of the Constrained Fitness Function.- 3.6 Test Problems.- 3.7 Implementation, Results and Discussions.- 3.7.1 Implementation.- 3.7.2 Results and Discussions.- 3.8 Summary.- 4. An Incest Prevented Evolution Strategy Algorithm.- 4.1 Introduction.- 4.2 Incest Prevention: A Natural Phenomena.- 4.3 Proposed Incest Prevented Evolution Strategy.- 4.3.1 Impact of Incest Effect on Variation Operators.- 4.3.2 Population Diversity and Similarity.- 4.3.3 Incest Prevention Method.- 4.4 Performance of the Proposed Incest Prevented Evolution Strategy.- 4.4.1 Case I: Test Functions for Comparison with GA, EP, ESs and NES.- 4.4.2 Case II: Test Functions for Comparison Between the NES and IPES Algorithms.- 4.5 Implementation and Experimental Results.- 4.5.1 Case I: Implementation and Results.- 4.5.2 Case II: Implementation and Results.- 4.6 Summary.- 5. Evolutionary Solution of Optimal Control Problems.- 5.1 Introduction.- 5.2 Conventional Variation Operators.- 5.2.1 Arithmetical Crossover/Intermediate Crossover.- 5.2.2 Uniform Mutation.- 5.3 Optimal Control Problems.- 5.3.1 Linear-Quadratic Control Problem.- 5.3.2 Push-Cart Control Problem.- 5.4 Simulation Examples.- 5.4.1 Simulation Example I: ESs with TVM and UM Operators.- 5.4.2 Simulation Example II: ESs with SBMAC and Conventional Methods.- 5.4.3 Implementation Details.- 5.5 Results and Discussions.- 5.5.1 Results for Example I.- 5.5.2 Results for Example II.- 5.5.3 Results from the Evolutionary Solution.- 5.6 Summary.- 6. Evolutionary Design of Robot Controllers.- 6.1 Introduction.- 6.2 A Mobile Robot with Two Independent Driving Wheels.- 6.3 Optimal Servocontroller Design for the Robot.- 6.3.1 Type-1 Optimal Servocontroller Design.- 6.3.2 Type-2 Optimal Servocontroller Design.- 6.4 Construction of the Fitness Function for the Controllers.- 6.4.1 Basic Notion.- 6.4.2 Method.- 6.5 Considerations for Design and Simulations.- 6.6 Results and Discussions.- 6.6.1 Design Results for Type-1 Controller.- 6.6.2 Design Results for Type-2 Controller.- 6.7 Summary.- 7. Evolutionary Behavior-Based Control of Mobile Robots.- 7.1 Introduction.- 7.2 An Evolution Strategy Using Statistical Information of Subgroups.- 7.2.1 Group Division.- 7.2.2 Max-mean Arit
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