PPT-Evolutionary Learning,

Part 2 Genetic algorithm design issues Representation of candidate solutions Fitness function Selection method Genetic operators crossover mutation etc Parameters Population size Crossover probability. com Evolutionary biology theory predicts that males usually wont invest a lot of time raising offspring when there is a good chance they are not the fathers Yale University researchers have found a notable exception to this premisea male fish in the Materialism. By Frank W. Elwell. Note:. This . presentation is based on the theories of Steven K. Sanderson. . as presented in his books listed in . the bibliography. . A more complete summary of . 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.. vs.. poker games. Yikan. Chen (yc2r@virginia.edu). Weikeng. Qin (wq7yt@virginia.edu). 1. Outline. 2. Evolutionary Algorithm. Poker!. Artificial Neural Network. E-ANN. Evolutionary algorithm. 3. Evolutionary algorithm. 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. 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. 1. Evolutionary Algorithms. CS 478 - Evolutionary Algorithms. 2. Evolutionary Computation/Algorithms. Genetic Algorithms. Simulate “natural” evolution of structures via selection and reproduction, based on performance (fitness). Larry Bull. UWE. Larry Bull. UWE. SEX. . A Social Interaction in Complex Intelligent Systems . Evolutionary Computing. F = . f. (A). F’ = . f. (A’). Nature. Hug et al. (2016) A new view of the tree of life. . Multi-objective Evolutionary Optimization. 1. Sources. “Handbook of Natural Computing,” Editors . Grzegorz. Rosenberg, Thomas Back and . Joost. N. . Kok. , Springer 2014. . “Multi-Objective Evolutionary Algorithms”, . . Multiobjective. . Optimization. . Algorithms. Karthik. . Sindhya. , . PhD. Postdoctoral Researcher. Industrial Optimization Group. Department of Mathematical Information Technology. Karthik.sindhya@jyu.fi. A discussion of the field of . evolutionary psychology. The study of behavior and mental function. Goal is to understand behavior . Psychology. Studies how evolution has. generated the diversity . of living organisms. 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 . Chapter. 2: . Evolutionary. Computing: the . Origins. Historical perspective. Biological inspiration:. Darwinian evolution theory . (simplified!). Genetics . (simplified!). Motivation for EC . 2.