Genetic Algorithms
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Course Title: Genetic Algorithms
Full Marks: 45 + 30
Course No: C.Sc. 665
Pass Marks: 22.5+15
Nature of the Course: Theory + Lab
Credit Hrs: 3
Course Objectives:
General introduction to the genetic algorithms and its literature, mathematical foundation: population, mutation, crossover. Data structure for genetic algorithms and current applications of genetic algorithms.
Course Contents:
Unit 1:
1.1 Introduction to Genetic Algorithm, Historical development, difference between traditional algorithms and genetic algorithms, mathematical foundation of genetic algorithm, building block hypothesis 8hrs
1.2 Primary data structures for genetic algorithm, reproduction, crossover, and mutation, mapping objective functions to fitness form, fitness scaling, coding, a multiparameter, mapped, fixed point coding, discretization, constrains into genetic algorithm search. 8hrs
1.3 The rise of genetic algorithms, genetic algorithm applications of historical interest, De Jong and function optimization, Improvement in basic techniques, current application of genetic algorithms. 7hrs
Unit 2
2.1 Advanced operators and techniques in genetic search: dominance, diploidy, and abeyance; Inversion and other reordering operators; other micro-operators, Niche and speciation, multi-objective optimization, knowledge-based techniques 12hrs
Unit 3
3.1 Genetic based machine learning, classifier system, rule and message system 4hrs
3.2 Application of genetic based machine learning, the rise of GBML, development of CS-1, Smith’s poker player 6hrs
Text Book:
David E. Goldberg, “Genetic algorithms in Search, Optimization, and Machine Learning”.
Reference Book:
Malanie Mitchell, “An Introduction to Genetic Algorithms”