PPT-Evolutionary

A lgorithms Andrew Cannon Yuki Osada Angeline Honggowarsito Contents What are Evolutionary Algorithms EAs Why are EAs Important Categories of EAs Mutation Self Adaptation Recombination. The NATURE. in the Nature vs. Nurture conundrum . Principles That define this level of analysis . Biological psychologists use the “Reductionist Approach”. They attempt to explain behavior very simply …. By finding the physiological (physical) reasons behind the behavior. 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. Phylogeny. The evolutionary history of a species or group of species. . Convergent Evolution. A process in which species from different evolutionary branches may come to resemble each other if they live in similar environments and natural selection favored similar adaptions.. Some slides are imported from . “Getting creative with evolution” from. P. Bentley, University College London. http://evonet.dcs.napier.ac.uk/summerschool2002/tutorials.html. http://en.wikipedia.org/wiki/Evolutionary_art. Chronological Overview. Early socialism (utopian socialism). Revolutionary . socialism (communism). Evolutionary . socialism (revisionism). Early socialism (utopian socialism). France. 1815. Saint-Simon, Fourier, Blanc, Proudhon. 6-1: . WHAT ARE . CHROMOSOMES, DNA, GENES. , AND THE HUMAN . GENOME. ? HOW DO BEHAVIOR GENETICISTS EXPLAIN OUR INDIVIDUAL DIFFERENCES?. Environment: . Every nongenetic influence, from prenatal nutrition to the people and things around us . . Multiobjective. . Optimization. . Algorithms. Karthik. . Sindhya. , . PhD. Postdoctoral Researcher. Industrial Optimization Group. Department of Mathematical Information Technology. Karthik.sindhya@jyu.fi. 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. Chapter. 2: . Evolutionary. Computing: the . Origins. Historical perspective. Biological inspiration:. Darwinian evolution theory . (simplified!). Genetics . (simplified!). Motivation for EC . 2. Behaviours are evolved responses to the environment in which the human species evolved.. There are two levels on which behaviours can be transmitted:. Genetic. Cultural. Timing information can inform as to which level generates a particular behaviour.. On Information . transforming. . systems. and. Critical . Realism. An individual. Is . developing. . and. . storing. . for. . future. . developing. In time. In a . selective. (. differentially. Module-III . B.Sc. 4. th. sem.. By. Dr. . Gyanranjan. . Mahalik. Asst. Prof.. Dept. of Botany; . SoAS. . Centurion University of Technology and Management . Systematics.  is the part of science that deals with grouping organisms and determining how they are related. It can be divided into two main branches. 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).