'Crossover rate' diaporamas de présentation

Chapter 10

838 views • 71 slides

Patterns in the DNA, RNA, whether they are functionally relevant, and how to find them

Patterns in the DNA, RNA, whether they are functionally relevant, and how to find them. Strand asymmetries (nucleotide frequencies). GC skew (inner circle, G-C/G+C) in a complete genome ( Nitrosomas_ europaea). GC skew switches often indicate the origin/terminus of replication in Bacteria.

765 views • 64 slides

Ehrhardt & Brigham

1.48k views • 63 slides

Capital Budgeting

Capital Budgeting. Chapter 13. Capital Budgeting. The process of planning investments in assets whose cash flows are expected to extend beyond one year. Importance of Capital Budgeting. Long term impact Timing

714 views • 46 slides

GA-based algorithms for finding equilibrium

549 views • 45 slides

Beyond Classical Search

Beyond Classical Search. Shanghai Jiao Tong University. Outline. Local search algorithms Hill-climbing search Simulated annealing search Local beam search Genetic algorithms. Local search algorithms.

477 views • 45 slides

Major Histocompatibility Complex Chapter 7

Major Histocompatibility Complex Chapter 7. MHC. Major Histocompatibility Complex Cluster of genes found in all mammals Its products play role in discriminating self/non-self Participant in both humoral and cell-mediated immunity MHC Act As Antigen Presenting Structures

737 views • 42 slides

Genetic algorithms Prof Kang Li

Email: K.Li@qub.ac.uk. Genetic algorithms Prof Kang Li. Last lecture RBF. This Lecture Genetic algorithm Basic GA operations. Content. What is Genetic Algorithm? How to implement GA? Encoding Selection Crossover Mutation A example Matlab demo Differential evolution.

570 views • 37 slides

Evolutionary algorithms vs. poker games

Evolutionary algorithms vs. poker games. Yikan Chen (yc2r@virginia.edu) Weikeng Qin (wq7yt@virginia.edu). Outline. Evolutionary Algorithm. E-ANN. Poker!. Artificial Neural Network. Evolutionary algorithm. Evolution Process. Evolutionary algorithm. Crossover. Mutation.

510 views • 35 slides

Introduction to Genetic Algorithms

456 views • 34 slides

Introduction to Genetic Algorithms

348 views • 34 slides

Genetics: Beyond Mendel

Genetics: Beyond Mendel. IB Biology. Mendelian Genetics. This is the term used to describe the basic principles of inheritance for traits that are not inherited in complicated ways. There are many exceptions to the principles we have learned with basic Mendelian genetics. Incomplete Dominance.

560 views • 29 slides

CHAPTER 11 The Basics of Capital Budgeting

497 views • 28 slides

A New Model of Distributed Genetic Algorithm for Cluster Systems: Dual Individual DGA

A New Model of Distributed Genetic Algorithm for Cluster Systems: Dual Individual DGA. Tomoyuki HIROYASU Mitsunori MIKI Masahiro HAMASAKI Yusuke TANIMURA. Doshisha University Kyoto, Japan. Cluster,Hyper Cluster, GRID. Tasks. Job.

352 views • 22 slides

MAE 552 Heuristic Optimization

MAE 552 Heuristic Optimization . Instructor: John Eddy Lecture #14 2/25/02 Evolutionary Algorithms. Practical Implementation Issues. The next set of slides will deal with some issues encountered in the practical implementation of a genetic algorithm.

214 views • 21 slides

Improvement of Multi-population Genetic Algorithms Convergence Time

Improvement of Multi-population Genetic Algorithms Convergence Time. Maria Angelova, Tania Pencheva maria.angelova@clbme.bas.bg , tania.pencheva@clbme.bas.bg. Fermentation processes.

306 views • 19 slides

2010. 05. 11 Jongwon Yoon

Evolutionary conditions for the emergence of communication in robots Dario Floreano , Sara Mitri , Stephane Magnenat , and Laurent Keller Current Biology , vol. 17, no. 6, pp. 514-519, 2007. 2010. 05. 11 Jongwon Yoon. Contents. Introduction Evolution of multiagent systems in robotics

272 views • 14 slides

Genetic Approach to Standard Cell Placement (GASP)

Genetic Approach to Standard Cell Placement (GASP). Using Meta-Genetic Parameter Optimization. What is a Genetic Algorithm?. Maintains a pool of solutions called the population Generates new solutions by combining genes from two parents. Parents are selected according to fitness

214 views • 8 slides

Capital Budgeting Problems

392 views • 6 slides