Intro; Preface; Contents; About the Authors; 1 Foundation in Evolutionary Optimization; 1.1 Optimization Problem-A Formal Definition; 1.2 Optimization Problems with and Without Constraints; 1.2.1 Handling Equality Constraints; 1.2.2 Handling Inequality Constraints; 1.3 Traditional Calculus-Based Optimization Techniques; 1.3.1 Gradient Descent Algorithm; 1.3.2 Steepest Descent Algorithm; 1.3.3 Newton's Method; 1.3.4 Quasi-Newton's Method; 1.4 Optimization of Discontinuous Function Using Evolutionary Algorithms; 1.4.1 Limitations of Derivative-Based Techniques

1.4.2 Emergence of Evolutionary Algorithms1.5 Selective Evolutionary Algorithms; 1.5.1 Genetic Algorithm; 1.5.2 Differential Evolution; 1.5.3 Particle Swarm Optimization; 1.6 Constraint Handling in Evolutionary Optimization; 1.7 Handling Multiple Objectives in Evolutionary Optimization; 1.7.1 Weighted Sum Approach; 1.7.2 Pareto Dominance Criteria; 1.7.3 Non-dominated Sorting Genetic Algorithm-II; 1.8 Performance Analysis of Evolutionary Algorithms; 1.8.1 Benchmark Functions and Evaluation Metrics for Single-Objective Evolutionary Algorithms

1.8.2 Benchmark Functions and Evaluation Metrics for Multi-objective Evolutionary Algorithms1.9 Applications of Evolutionary Optimization Algorithms; 1.10 Summary; References; 2 Agents and Multi-agent Coordination; 2.1 Defining Agent; 2.2 Agent Perception; 2.3 Performance Measure of Agent; 2.4 Agent Environment; 2.5 Agent Architecture; 2.5.1 Logic-based Architecture; 2.5.2 Subsumption Architecture; 2.5.3 Belief-Desire-Intention Architecture; 2.5.4 Layered Architecture; 2.6 Agent Classes; 2.6.1 Simple Reflex Agent; 2.6.2 Model-based Reflex Agent; 2.6.3 Goal-based Agent

2.6.4 Utility-based Agent2.6.5 Learning Agent; 2.7 Multi-agent System; 2.8 Multi-agent Coordination; 2.9 Multi-agent Planning; 2.10 Multi-agent Learning; 2.11 Evolutionary Optimization Approach to Multi-agent Robotics; 2.12 Evolutionary Optimization Approach to Multi-agent Robotics in the Presence of Measurement Noise; 2.13 Summary; References; 3 Recent Advances in Evolutionary Optimization in Noisy Environment- A Comprehensive Survey; 3.1 Introduction; 3.2 Noisy Optimization Using Explicit Averaging; 3.2.1 Time-Based Sampling; 3.2.2 Domination Strength-Based Sampling

3.2.3 Rank-Based Sampling3.2.4 Standard Error Dynamic Resampling (SEDR); 3.2.5 m-Level Dynamic Resampling (mLDR); 3.2.6 Fitness-Based Dynamic Resampling (FBDR); 3.2.7 Hybrid Sampling; 3.2.8 Sampling Based on Fitness Variance in Local Neighborhood; 3.2.9 Progress-Based Dynamic Sampling; 3.2.10 Distance-Based Dynamic Sampling; 3.2.11 Confidence-Based Dynamic Resampling (CDR); 3.2.12 Noise Analysis Selection; 3.2.13 Optimal Computing Budget Allocation (OCBA); 3.3 Effective Fitness Estimation; 3.3.1 Expected Fitness Estimation Using Uniform Fitness Interval