CM7012 EVOLUTIONARY COMPUTATION Syllabus | Anna University ME

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Anna University Syllabus - Anna University ME Syllabus

CM7012 EVOLUTIONARY COMPUTATION Syllabus | Anna University ME Computer Integrated Manufacturing Second Semester Syllabus Regulation 2013. Below is the Anna University 2013 Regulation Syllabus for 2nd Semester for ME Computer Integrated Manufacturing, Textbooks, Reference books, Exam portions, Question Bank, Previous year question papers, Model question papers, Class notes, Important 2 marks, 8 marks, 16 marks topics.
It is applicable for all students admitted in the Academic year 2013-2014 onwards for all its Affiliated institutions in Tamil Nadu.

CM7012 EVOLUTIONARY COMPUTATION L T P C 3 0 0 3

OBJECTIVE:
 To impart the knowledge in optimization, multi objective optimization, evolutionary algorithms,
Multi-Objective Evolutionary Algorithms and programming.
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UNIT I INTRODUCTION TO OPTIMIZATION: 9
Introduction to optimization - single and multi objective optimization - Evolutionary algorithms -
principles of multi objective optimization.
UNIT II MULTI OBJECTIVE OPTIMIZATION: 9
Convex programming, Karush-Kuhn-Tucker conditions, Direct functional evaluation and derivative
based optimization techniques;
UNIT III EVOLUTIONARY ALGORITHMS: 9
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 9
Evolutionary strategies, Evolutionary programming, genetic programming, Differential evolution,
Particle swarm optimization;
UNIT V APPLICATIONS OF MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS: 9
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.
TOTAL: 45 PERIODS
OUTCOME:
On completion of the course the students will be able to apply optimization using techniques like
evolutionary strategies and evolutionary programming.
REFERENCES
1. Deb, K., “Multi-objective Optimization using Evolutionary Algorithms”, Wiley, 2001.
2. Clerc, M.,”Particle Swarm Optimization”, ISTE, 2006.
3. Back, T., Fogal, D. B. and Michalewicz, Z., “Handbook of Evolutionary Computation”, Oxford
University Press, 1997.
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.