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Principles in noisy optimization : applied to multi-agent coordination

Author: Pratyusha Rakshit; Amit Konar
Publisher: Singapore : Springer, 2018.
Series: Cognitive intelligence and robotics
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
Noisy optimization is a topic of growing interest for researchers working on mainstream optimization problems. Although several techniques for dealing with stochastic noise in optimization problems are covered in journals and conference proceedings, today there are virtually no books that approach noisy optimization from a layman's perspective; this book remedies that gap. Beginning with the foundations of  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Rakshit, Pratyusha.
Principles in noisy optimization.
Singapore : Springer, 2018
(OCoLC)1022077706
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Pratyusha Rakshit; Amit Konar
ISBN: 9789811086427 9811086427
OCLC Number: 1076574353
Description: 1 online resource (xvi, 367 pages) : illustrations (some color)
Contents: 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
Series Title: Cognitive intelligence and robotics
Responsibility: Pratyusha Rakshit, Amit Konar.

Abstract:

Noisy optimization is a topic of growing interest for researchers working on mainstream optimization problems. In turn, Chapter 3 provides an extensive review of noisy optimization, while Chapter 4  Read more...

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