PPT-Introduction to Evolutionary Computation

Questions to consider during this lesson How is digital evolution similar to biological evolution How is it different How can the principles of natural selection be applied to solve engineering design problems. 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. Project Review 12 July 2013. Projects. Modelling. . dragonfly attention switching. Dendritic auditory processing. Processing images . with . spikes. Dendritic . computation with . memristors. . Computation in RATSLAM. 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.. Week 5. General feedback: Thought Paper #1. A thought paper is about . your . reasoned . thoughts. If 40% of your TP is your summary, you are losing ~40% of your marks. I do not grade introductions (but you still need them). 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. 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”, . 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 . 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 . 1. Behavior Genetics and Evolutionary Psychology. Behavior Genetics: Predicting Individual Differences. Genes: Our Codes for Life. Twin and Adoption Studies . Temperament and Heredity. Nature . and. Nurture. First lecture: Introduction to Evolutionary Computation. Second lecture: Genetic Programming. Inverted CERN School of Computing . 2017. Daniel Lanza - CERN. Agenda. Introduction to Evolutionary Computati. On Information . transforming. . systems. and. Critical . Realism. An individual. Is . developing. . and. . storing. . for. . future. . developing. In time. In a . selective. (. differentially. 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).