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Introduction to Genetic Algorithm
This document introduces genetic algorithms as part of soft computing methods. It explains that soft computing includes fuzzy systems, neural networks, and evolutionary algorithms. Genetic algorithms are evolutionary algorithms that use processes inspired by biological evolution, like inheritance, mutation, selection, and crossover. The document provides flowcharts demonstrating the basic processes in a genetic algorithm, including initial population, selection, crossover, mutation, and output of best individuals. It also shows examples of single-point, two-point, and multi-point crossover techniques.
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Introduction to Genetic Algorithm
- 1. Introduction: Genetic Algorithm AdriJovin J J, M.Tech., Ph.D. UITE221- SOFT COMPUTING
- 2. Soft Computing • Introducedby Lotfi A. Zadeh, University of California, Berkley • Collection of computational methods • Includes Fuzzy Systems, Neural Networks and Evolutionary Algorithms • Deployment of soft computing for the solution of machine learning problems has led to high Machine Intelligence Quotient UITE221 SOFT COMPUTING 2 Image Credit: Electrical Engineering and Computer Sciences, UC, Berkeley “Soft computing differs from hard computing (conventional computing) in its tolerance to imprecision, uncertainty and partial truth” -Lotfi A. Zadeh
- 3. Soft Computing (Contd…) FuzzySystems Neural Networks Evolutionary Algorithms UITE221 SOFT COMPUTING 3 Fuzzy-evolutionary hybrids Neuro-fuzzy hybrids Neuro-evolutionary hybrids Neuro-fuzzy-evolutionary hybrids
- 4. Flowchart for geneticalgorithm UITE221 SOFT COMPUTING 4 Start Initial Population Selection Crossover Mutation Terminate? Best Individuals Output Stop Evolution Yes No
- 5. Single-site Crossover UITE221 SOFTCOMPUTING 5 This Photo by Unknown Author is licensed under CC BY-SA
- 6. Two-point crossover UITE221 SOFTCOMPUTING 6 This Photo by Unknown Author is licensed under CC BY-SA
- 7. Multi-point crossover UITE221 SOFTCOMPUTING 7 This Photo by Unknown Author is licensed under CC BY-SA
- 8. References Rajasekaran, S., &Pai, G. V. (2017). Neural Networks, Fuzzy Systems and Evolutionary Algorithms: Synthesis and Applications. PHI Learning Pvt. Ltd.. Haykin, S. (2010). Neural Networks and Learning Machines, 3/E. Pearson Education India. Sivanandam, S. N., & Deepa, S. N. (2007). Principles of soft computing. John Wiley & Sons. UITE221 SOFT COMPUTING 8