EVOLUTIONARY ALGORITHMS

Mumbai University-Fourth / Final Year -Semester VII Information Technology Syllabus (Revised) EVOLUTIONARY ALGORITHMS

Elective – I : EVOLUTIONARY ALGORITHMS
CLASS B.E. ( INFORMATION TECHNOLOGY) SEMESTER VII
HOURS PER WEEK	LECTURES	:	04
TUTORIALS	:	--
PRACTICALS	:	02
	HOURS	MARKS
EVALUATION SYSTEM:	THEORY		3	100
PRACTICAL		--	--
ORAL		--	25
TERM WORK		--	25
Prerequisite: Data Structures and Algorithms, Knowledge of Programming Language / Tool (c / c++ / Java).
Objective: The objective of the course is to understand the working of Evolutionary algorithms such as Genetic Algorithm, Genetic Programming, Evolutionary Algorithms and Evolutionary Programming with their application is the various aspects of Computer engineering.

1. Evolutionary Computation (EC): The Historical Development of EC, Principles of Darwinian natural selection, Overview of Genetic Algorithms (GA), Genetic Programming (GP), Evolutionary Strategies (ES), Evolutionary Programming (EP), Features of Evolutionary Computation, Genes and Population Genetics, The Genotype/Phenotype Dichotomy, Broad Applicability, Hybridization with Other Methods, Parallelism, Applications of Evolutionary Computation.

2. Genetic Algorithms (GA): Overview of Conventional Optimization and Search Techniques, Simple Genetic Algorithm, Comparison with Other Optimization Techniques, Application of GA (Data analysis and prediction, Genetic algorithms in financial markets, GA in search, optimization, and machine learning), GA Terminologies: Individual, Genes, Fitness, Population, Encoding, Breeding, Termination Implicit Parallism, Case Study of Traveling Salesman Problem.

3. Advanced Operators in GA: Diploidy, Dominance and Abeyance, Multiploid, Inversion and Reordering, Niche and Speciation, Micro-operators, Non-binary Representation, Multi-objective Optimization, Combinatorial Optimization, GA classifications: SGA, Parallel GA, Hybrid GA.

4. Genetic Programming (GP): Introduction, Comparison with GA, Primitives of GP, Attributes, Terminals, Function set, Operators in GP, Steps in GP, Improving genetic programming with statistics, Genetic programming with tree genomes, linear genomes, and graph genomes, Implementation of genetic programming , GP Applications. Case study of Santa-Fe-trial, Case study of John Muir Trail.

5. Foundations of Evolutionary Algorithms: Schemas and the two-armed bandit problem, Mathematical models for simple genetic algorithms, Where to use evolutionary algorithms? Theoretical advantages and disadvantages of evolutionary algorithms over alternative methods (hill-climbing, simulated annealing, etc.), Co-evolutionary Algorithms: Cooperative co-evolution, Competitive co-evolution, Swarm intelligence and ant colony optimization.

6. Evolutionary Strategies (ES): Introduction, Comparison with GA & GP, Operators, Gaussian Mutation Operator, Intermediate Recombination Operator, Application of ES for Image Enhancement.

7. Evolutionary Programming (EP): Introduction, Comparison with GA, GP & ES. Selection mechanism, Applications of ES.

8. Multi-Objective Evolutionary Optimization: Pareto optimality, Multi-objective evolutionary algorithms. Learning Classifier Systems: Basic ideas and motivations, Main components and the main cycle. Theoretical Analysis of Evolutionary Algorithms: Schema theorems, Convergence of EAs, Computational time complexity of EAs, No free lunch theorem.

9. Application of Genetic Algorithm to Image Processing: Designing Texture Filters with Genetic Algorithms, Genetic Algorithm Based Knowledge Acquisition on Image Processing, Object Localization in Images Using Genetic Algorithm, Problem Description, Image Preprocessing, The Proposed Genetic Algorithm Approach.

Text Book:

1. Sivanandam, Deepa “Introduction to Genetic Algorithm”, Springer.

2. Melanie Mitchell: “An Introduction to Genetic Algorithm”, PHI.

Reference Books:

1. D. E. Goldberg, “Genetic Algorithms in Search, Optimisation and Machine Learning”, Addison-Wesley.

2. Zbigniew Michalewics, "Genetic Algorithms + Data Structures = Evolution Programs", Springer Verlag, 1997.

3. Goldberg, “Genetic Algorithms”, Pearson Eduction.

4. T. Back, D. B. Fogel and Michalewicz, "Evolutionary Computation1: Basic Algorithms and Operators", 2000.

5. A. E. Eiben and J.E. Smith, “Introduction to Evolutionary Computing”, Springer, 2003.

6. W. Banzhaf et al. Morgan Kaufmann, “Genetic Programming: An Introduction”, 1999.

7. J. R. Koza, “Genetic Programming: On the Programming of Computers by Means of Natural Selection”, 1992

8. Vose Michael D, “The Simple Genetic Algorithm — Foundations And Theory”, Phi.

9. Rajasekaran S. , Pai G.A. Vijayalakshmi , “Neural Networks, Fuzzy Logic, and Genetic Algorithms: Synthesis and Applications”, Phi.

10. Reeves, C. R. and Rowe, J. E., “Genetic Algorithms - Principles and Perspectives: A Guide to GA Theory”, 2003.

11. Falkenauer. E., “Genetic Algorithms and Grouping Problems”, 1998.

Term Work: Term work shall consist of at least 10 experiments covering all topics and one written test. Distribution of marks for term work shall be as follows: Attendance (Theory and Practical) 05 Marks Laboratory work (Experiments and Journal) 10 Marks Test (at least one) 10 Marks The final certification and acceptance of TW ensures the satisfactory Performance of laboratory Work and Minimum Passing in the term work. Suggested Experiment list

A mini-project based on the following (not Restricted to) topic:

• Flow Shop Scheduling Problem.

• Traveling Sales-person Problem.

• Santa-Fe-trial.

• John Muir Trail.

• Designing Texture Filters with Genetic Algorithm.

• Knowledge Acquisition on Image Processing.

• Object Localization in Images Using Genetic Algorithm.

• Finite Automata Construction Using Genetic Algorithm.

• Russian Roulette

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