PPT-CS 478 - Evolutionary Algorithms

1 Evolutionary Algorithms CS 478 Evolutionary Algorithms 2 Evolutionary ComputationAlgorithms Genetic Algorithms Simulate natural evolution of structures via selection and reproduction based on performance fitness. Steven . M. . Roels. Department . of Zoology, Michigan State . University. Introduction. “. It follows that naturalistic evolution will not attract a majority of Americans until our nation becomes less religious.” – . 1. Unsupervised Learning and Clustering. In unsupervised learning you are given a data set with no output classifications. Clustering is an important type of unsupervised learning. PCA was another type of unsupervised learning. 1. Ensembles. CS 478 - Ensembles. 2. A “Holy Grail” of Machine Learning. Automated. Learner. Just a . Data Set. or. just an. explanation. of the problem. Hypothesis. Input Features. Outputs. CS 478 - Ensembles. Khaled Rasheed. Computer Science Dept.. University of Georgia. http://www.cs.uga.edu/~khaled. Genetic algorithms. Parallel genetic algorithms. Genetic programming. Evolution strategies. Classifier systems. 1. Learning Sets of Rules. CS 478 - Learning Rules. 2. Learning Rules. If (Color = Red) and (Shape = round) then Class is A. If (Color = Blue) and (Size = large) then Class is B. Natural . and intuitive hypotheses. Firearms Import/Export Conference. July 31 – August 1, 2012. Active Federal Firearms Licenses. Type. Number. Percent. 01-Dealer. 50,224. 38.86. 02-Pawnbroker. 7,318. 5.66. 03-Collector. 61,419. 47.52. Dr. . Jagdish. . kaur. P.G.G.C.,Sector. 11. , Chandigarh. . . SIMPLY THE CHANGES OVER TIME. EVOLUTION. Human Evolution. The evolutionary timeline is divided into sections of time called eras – which are then divided into smaller units of time called periods.. 100 kg. 75 kg. F = G (m. 1. m. 2. / r. ²). m. 1. = 100 kg. m. 2. = 75 kg. r = 2 m. 2 m. F = 6.67 x 10 . –11. . Nm. ²/kg² . (100kg * 75kg) / (2m)². = 1.25 E -7 N. UGA EXAMPLE 1. Two particles are separated in space with a center to center distance of 0.478 m. The mass of A is 365 kg and the mass of B is 765 kg. Find the magnitude and direction of the net gravitational force acting on the particles.. 5.1. Definition : Requirements. “. Requirements are capabilities and conditions to which the system, and more broadly the project, must conform. ”. The UP does not attempt to fully define the requirements before programming but instead, promotes a systematic approach to finding, documenting, . appeared in the 1950s and 1960s. used to find approximations in search problems. use principles of natural selection to find an optimized solution. part of evolutionary algorithms. What is it?. subset of evolutionary computation. Evo. . Psyc. is the application of Darwinian principles to the understanding of human nature..  . To understand how Darwinian principles are applied to humans one must first understand a number of concepts and premises upon which . Evo. . Psyc. is the application of Darwinian principles to the understanding of human nature..  . To understand how Darwinian principles are applied to humans one must first understand a number of concepts and premises upon which . What you will learn. Common traits of problems which can be solved by EAs efficiently. “HUMIES” competition with few examples of winning solutions of various problems. When EAs can be competitive with Reinforcement Learning techniques when solving various control problems. 1. Evolutionary Algorithms. CS 472 - Evolutionary Algorithms. 2. Evolutionary Computation/Algorithms. Genetic Algorithms. Simulate “natural” evolution of structures via selection and reproduction, based on performance (fitness).