CM4012 Evolutionary Computation Syllabus:
CM4012 Evolutionary Computation Syllabus – Anna University PG Syllabus Regulation 2021
OBJECTIVES:
To impart the knowledge in optimization,
To explore multi objective optimization,
To learn evolutionary algorithms,
To understand Evolutionary strategies and programming.
To get familiarized in Multi-Objective Evolutionary algorithm
UNIT I INTRODUCTION TO OPTIMIZATION
Introduction to optimization – single and multi objective optimization – Evolutionary algorithms – principles of multi objective optimization.
UNIT II MULTI OBJECTIVE OPTIMIZATION
Convex programming, Karush-Kuhn-Tucker conditions, Direct functional evaluation and derivative based optimization techniques
UNIT III EVOLUTIONARY ALGORITHMS
Simulated annealing, Tabu search; NFL theorem; Biological principles of evolution, General scheme of EAs, Representation, Selection schemes, Population evaluation, Variation operators; Constraint handling; Schema theorem; Binary coded genetic algorithm, Real coded genetic algorithm.
UNIT IV EVOLUTIONARY STRATEGIES AND EVOLUTIONARY PROGRAMMING
Evolutionary strategies, Evolutionary programming, genetic programming, Differential evolution, Particle swarm optimization
UNIT V APPLICATIONS OF MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS
Pareto-optimality, Multi-objective evolutionary algorithms; Statistical analysis of EC techniques; Customization in EAs; Applications of multi-objective evolutionary algorithms – Mechanical component design – Truss-structure design – Other applications.
OUTCOME:
CO1: Demonstrate principles of optimization process
CO2: Make use of multi response optimization
CO3: Inference to the evolutionary programming
CO4: Evaluate the process parameters for optimization
CO5: Apply optimization techniques in mechanical component design
REFERENCES
1. Back, T., Fogal, D. B. and Michalewicz, Z., “Handbook of Evolutionary Computation”, Oxford University Press, 1997.
2. Clerc, M.,”Particle Swarm Optimization”, ISTE, 2006.
3. Deb, K., “Multi-objective Optimization using Evolutionary Algorithms”, Wiley, 2001.
4. Fogel, D. B., “Evolutionary Computation, The Fossil Record”, IEEE Press, 2003.
5. Goldberg, D., “Genetic Algorithms in Search, Optimization, and Machine Learning”, Addison Wesley, 1989.
6. Price, K. , Storn, R. M. , and Lampinen, J. A. ,”Differential Evolution: A Practical Approach to Global Optimization”, Springer, 2005.