Evolutionary Computation for Single and Multi-Objective Optimization
Indian Institute of Technology Guwahati and NPTEL via Swayam
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Overview
INTENDED AUDIENCE :Final and Pre-final year UG students, PG Students and Candidates from IndustriesPREREQUISITES : Elementary Mathematics and ProgrammingINDUSTRIES SUPPORT :All R&D industries that involve design and optimization of product and system
Syllabus
COURSE LAYOUT
Week 1:Introduction and Principles of Evolutionary Computation (EC):Introduction to Optimization, Generalized Formulation, Scope of Optimization via Applications, Characteristic of Optimization Functions;Principles of EC: Natural Evolutional and Genetics, Generalized Framework, Behavior and Typical run of EC, Advantages and LimitationsWeek 2:Binary-Coded Genetic Algorithm (BGA): Introduction, Binary Representation and Decoding, Working Principle of binary coded GA (BGA), BGA on Generalized Framework,Operators, Hand Calculations, Graphical Examples;Week 3:Real-Coded Genetic Algorithm (RGA): Concepts and Need of Real-Coded GA (RGA), Algorithm, RGA on Generalized Framework, Operators, Hand Calculations, Graphical Examples, Case studies;Week 4:Other EC Techniques: Differential Evolution (DE): Introduction, Concepts, Operators, Algorithm, DE on Generalized Framework, Graphical Examples, Case studies; Particle Swarm Optimization (PSO): Introduction, Concepts, Operators, PSO on Generalized Framework, Graphical Examples, Case studies;Week 5:Constraint Handling Techniques : Generalized Constraint Formulation, Karush Kuhn Tucker (KKT) conditions, Penalty Function Method, Parameter-Less Deb’s Method, Hand Calculations, Graphical Examples, Case studiesWeek 6 Introduction to Multi-Objective Optimization : Introduction, Generalized Formulation, Concept of Dominance and Pareto-optimality, Graphical Examples, Terminologies, Difference with Single-objective optimization, Approaches to multi-objective optimization;Week 7:Classical Multi-Objective Optimization Methods : Classical Multi-Objective Optimization Methods: Weighted- Sum Method, ε-Constraint Method, Weighted Metric Methods, Hand Calculations, Difficulties with Classical approaches, Ideal Multi- Objective Optimization Approach;Week 8:Multi-Objective Evolutionary Algorithms (MOEAs): Introduction, MOEAs on generalized Framework, Algorithms: NSGA-II, SPEA2, Graphical Examples, Case Studies; Hypervolume Indicator (HV) for Performance Assessment
Taught by
Prof. Deepak Sharma
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