Genetic Algorithms and Applications ECSyllabus for
EC-457 Genetic Algorithms and Applications [3 0 0 3]
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.
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
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
5. William M, “Evolutionary Algorithms: The Role of Mutation and Recombination”,(Natural
Computing Series), Springer-Verlag (2000)