Genetic Algorithms

Unit 1

Introduction: Robustness of Traditional Optimization and Search Methods, The goals of Optimization, How are Genetic Algorithms Different from Traditional Methods?, A simple genetic algorithm, Genetic algorithms at work - a simulation by hand, Grist for the Search Mill - important similarities, Similarity templates (Schemata), Learning the Lingo.

Unit 2

Genetic Algorithms Revisited: Mathematical Foundations - Who shall live and who shall die?

The fundamental theorem, Schema Processing at work: An example by hand revisited, the two- armed and K-armed bandit problem, How many schemata are processed usefully?, The building block hypothesis, another perspective: the minimal Deceptive problem, Schemata revisit:

similarity templates as hyper planes.

Unit 3

Computer Implementation Of A Genetic Algorithm - Data structures, reproduction, crossover, and mutation, A time to reproduce, a time to cross, get with the main program, How well does it work? Mapping objective functions to fitness form, fitness scaling, codings, a multiparameter, mapped, fixed-point coding, discretization, Constraint Handling.

Unit 4

Techniques In Genetic Search - Dominance, diploidy and abeyance, inversion and other reordering operators, other micro operators, niche and speciation, multiobjective optimization - Knowledge-based techniques, genetic algorithms and parallel processors.

Unit 5

Multi objective evolutionary optimization: Pareto optimality, multi-objective evolutionary algorithms: MOGA, NSGA-II, etc. Applications of GA in engineering problems, job-shop scheduling and routing problems

REFERENCES:

1. David E.Goldberg, “Genetic Algorithms” - 1/e, Pearson Education.

2. Genetic algorithms in search, optimization and Mechine learning, By David E. Gold Berg Pearson Edition