Cs161 Genetic Algorithms l-t-p-cr: 3-0-3 Objectives

CS161 Genetic Algorithms

L-T-P-Cr: 3-0-3

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

Text Books:

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)