Objectives: The objective of the course is to introduce the role of nature–inspired algorithms in computationally hard problems.
Pre-requisite: Computer Algorithms
Outcome: By the end of the course, students should:
appreciate the role of using nature-inspired algorithms in computationally hard problems,
be able to apply what they learnt across different disciplines,
Appreciate the emergence of complex behaviours in networks not present in the individual network elements.
UNIT I Lectures: 12
Introduction to Evolutionary Computation (EC): Biological and artificial evolution, Different branches of EC, e.g., GAs, EP, ES, GP, etc. A simple evolutionary algorithm Search Operators: Recombination/ Crossover for strings (e.g. binary strings), e.g., one point, multipoint and uniform crossover operators, Mutation for strings, e.g., bit flipping, recombination/crossover and mutation rates, Recombination for real –valued representations, e.g. discrete and intermediate recombinations, Mutation for real-valued representations, e.g., Gaussian and Cauchy mutations, self-adaptive mutations, etc. Why and how a recombination or mutation operator works.
UNIT II Lectures: 20
Selection Schemes: Fitness Proportional selection and fitness scaling, Ranking, including linear, power, exponential and other ranking methods, Tournament selection, Selection pressure and its impact on evolutionary search. Search Operators and Representations: Mixing different search operators, an anomaly of self-adaptive mutations, the importance of representation, e.g., binary vs. Gray Coding, Adaptive representation, Analysis, some examples
UNIT III Lectures: 10
Multiobjective Evolutionary Optimization: Pareto optimality, Multiobjective evolutionary algorithms, computational time complexity of EAs, No free lunch theorem Some Applications
1. David A Coley, “An introduction to Genetic Algorithms for Scientists and Engineers”, World scientific publishing company(1997)
2. Mitsuo Gen Runwei Cheng, Wiley-Interscience, “Genetic Algorithms and Engineering Design”, 1st Edition, (1997)
3. Thomas Back, “Evolution algorithms in theory and practice evolution strategies, Evolutionary programming, Genetic Algorithms”, Oxford University press,(1996)
4. Kalyanmoy Deb, “ Multi Objective Optimization using Evolutionary Algorithms”, John Wiley and Sons(2001)
5. William M, “Evolutionary Algorithms: The Role of Mutation and Recombination”,(Natural Computing Series), Springer-Verlag (2000)