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# Genetic Algorithm for optimization on IRIS Dataset presentation ppt

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Apply the Genetic Algorithm for optimization on a dataset obtained from UCI ML repository.

For Example: IRIS Dataset
Genetic Algorithm Optimization, Iris Dataset, Machine Learning, Python.

Apply the Genetic Algorithm for optimization on a dataset obtained from UCI ML repository.

For Example: IRIS Dataset
Genetic Algorithm Optimization, Iris Dataset, Machine Learning, Python.

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### Genetic Algorithm for optimization on IRIS Dataset presentation ppt

1. 1. Department of Computer Engineering Sandip Foundation's Sandip Institute of Technology and Research Centre, Nashik Savitribai Phule Pune University LP-III MINI PROJECT Year 2019 – 2020 Under the Guidance Prof. Mangesh Ghonge
2. 2. Optimization of Genetic Algorithm using Iris Flower Dataset PRESENTED BY:- G23 - Sunil Rajput Exam No: 71720728F - Ashish kumar Singh Exam No: 71324943K - Ashish Yadav Exam No: 71741665J - Mayank Patil Exam No: 71550097L
3. 3. OPTIMIZATION It’s a procedure to make a system or design as effective, especially involving the mathematical techniques. To minimize the cost of production or to maximize the efficiency of production.
4. 4. GENETIC ALGORITHM A genetic algorithm (or short GA) isa search technique used in computing to find true or approximate solutions to optimization and search problems. Genetic algorithms are categorized as global search heuristics. Genetic algorithms are a particular class of evolutionary algorithms.
5. 5. G A PROCEDURE A typical genetic algorithm requires two things to be defined: a genetic representation of the solution domain. a fitness function to evaluate the solution domain.
6. 6. What Do We Mean By Genetic Algorithm? It is started with a set of randomly generated solutions and recombine pairs of them at random to produce offspring. Only the best offspring and parents are kept to produce the next generation.
7. 7. PROBLEM DOMAINS  Problems which appear to be particularly appropriate for solution by genetic algorithms include timetabling and scheduling problems,  Genetic algorithms are often applied as an approach to solve global optimization problems.  As a general rule of thumb genetic algorithms might be useful in problem domains that have a complex fitness landscape as recombination is designed to move the population away from local optima that a traditional hill climbing algorithm might get stuck in.
8. 8. Best known database to be found in the pattern recognition literature. Data set- Iris flower data set(Donated date - 1988-07-01), also known as Fisher's Iris data set and Anderson's Iris data set b/c Edgar Anderson collected the data. It is multivariate(more than 2 dependent variable) data set Study of three related Iris flowers species. Data set contain 50 sample of each species(Iris-Setosa, Iris-Virginica, Iris- Versicolor)
9. 9. Sepal length in cm Sepal width in cm Petal length in cm Petal width in cm Min Max Mean SD Class Correlation sepal length: 4.3 7.9 5.84 0.83 0.7826 sepal width: 2.0 4.4 3.05 0.43 -0.4194 petal length: 1.0 6.9 3.76 1.76 0.9490 (high!) Petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)
10. 10. Classify a new flower as belonging to one of the 3 classes given the 4 features
11. 11. # Box and whisker plots(Give idea about distribution of input attributes)
12. 12. 1.Using Petal_Lenght & Petal_Width features, we can distinguish Setosa, Versicolor & Virginica fairly 2.There are slightly overlap of Versicolor & Virginica. 3.Graph shows that Petal (Length and Width) features are best contributor for Iris Species as compare to Sepal (Length and Width)
13. 13. 4 Evaluate by using 6different Algorithms(Cross Validation) Here, 1. Logistic Regression (LR) 2. Linear Discriminant Analysis(LDA) 3. K-Nearest Neighbour(KNN) 4. Classification and Regression Tree(CART) 5. Gaussion Naive Bayes(NB) 6. Support Vector Machine(SVM)
14. 14. Case Features used Best Model Train Accuracy Test Accuracy Missclassified 1 All features in SVM .9899 .9555 2 classes 2 Sepal only SVM .8472 .7111 12 3 Petal only SVM .9899 .9333 3 4 PetalWidth,Sepal (Len,Wid) SVM/LDA .9809 .9111 4 5 PetalLen,Sepal (Len,Wid) SVM .9700 .9111 4
15. 15. Application :  Software engineering.  Traveling Salesman Problem.  Mobile communications infrastructure optimization.  Electronic circuit design, known as Evolvable hardware.
16. 16. Advantages : A GA has a number of advantages.  It can quickly scan a vast solution set.  Bad proposals do not effect the end solution negatively as they are simply discarded.  The inductive nature of the GA means that it doesn't have to know any rules of the problem - it works by its own internal rules.  This is very useful for complex or loosely defined problems.
17. 17. Disadvantages : A practical disadvantage of the genetic algorithm involves longer running times on the computer. Fortunately, this disadvantage continues to be minimized by the ever-increasing processing speeds of today's computers.
18. 18.  Conclusion: Evolutionary algorithms have been around since the early sixties. They apply the rules of nature: evolution through selection of the fittest individuals, the individuals representing solutions to a mathematical problem.Genetic algorithms are so far generally the best and most robust kind of evolutionary algorithms.
19. 19. REFERENCES 1. Akbari Z. (2010). "A multilevel evolutionary algorithm for optimizing numerical functions" IJIEC 2 (2011): 419–430 2. Ananya (2017), What is Diabetes, retrieved online from https://www.news- medical.net/health/What- is-Diabetes.aspx 3. Coffin, D.; S., Robert E. (2008). "Linkage Learning in Estimation of Distribution Algorithms". Linkage in Evolutionary Computation. Springer Berlin Heidelberg: 141– 156. doi:10.1007/978-3-540- 85068-7_7. 4. Eiben, A. E. et al (1994). Genetic algorithms with multi-parent recombination, PPSN III: Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: 78–87. ISBN 3-540-58484-6. 5. Clustering - K-means demo’, K-means-Ineractive demo, Available at: http://home.deib.polimi.it/matteucc/Clustering/tutorial_html/AppletKM.html. Consulted 22 AUG 2013 6. Bache, K.& Lichman, M. 2013. UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science. 7. Bishop, C. 2006. Pattern Recognition and Machine Learning. New York: Springer, pp.424- 428. 8. Fisher, R.A. 1936. UCI Machine Learning Repository: Iris Data Set. Available at: http://archive.ics.uci.edu/ml/datasets/Iris. Consulted 10 AUG 2013 9. Mitchell, T. 1997. Machine learning. McGraw Hill.
20. 20. Thanking you