Building on Handbook of Machine Learning — Volume 1: Foundation of Artificial Intelligence, this volume on Optimization and Decision Making covers a range of algorithms and their applications. Like the first volume, it provides a starting point for machine learning enthusiasts as a comprehensive guide on classical optimization methods. It also provides an in-depth overview on how artificial intelligence can be used to define, disprove or validate economic modeling and decision making concepts.
Contents: IntroductionClassical OptimizationGenetic AlgorithmParticle Swarm OptimizationSimulated AnnealingResponse Surface MethodAnt Colony OptimizationBat and Firefly AlgorithmsArtificial Immune SystemInvasive Weed Optimization and Cuckoo Search AlgorithmsDecision Trees and Random ForestsHybrid MethodsEconomic ModelingCondition MonitoringRational Decision-MakingConcluding Remarks
Readership: This book is a useful reference for students and practitioners in artificial intelligence.
