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Evolving objects

Evolving objects

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Evolving objects

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  1. Evolving objects JJ Merelo jmerelo@kal-el.ugr.es

  2. Evolution • Evolution in Nature • Market evolution • Evolving inflationary universes • Memetic evolution Http:// .ugr.es

  3. Evolution is not about substratesit´s aboutinterfacesandsurvival of the fittest Http:// .ugr.es

  4. Artificial evolution • Artificial evolution paradigms focus on representation Http:// .ugr.es

  5. Evolvable objects: summary • Objects can replicate (or be replicated), and create other objects. • Some objects can change other objects (create diversity) • Some objects can combine or eliminate other objects (decrease diversity) • Objects can be evaluated, or at least, compared to each other, that is, they have a fitness or cost.     Http:// .ugr.es

  6. Evolvability conditions • Objects can replicate (or be replicated), and create other objects. Http:// .ugr.es

  7. Evolvability conditions • Some objects can change other objects (create diversity) Http:// .ugr.es

  8. Evolvability conditions • Some objects can combine or eliminate other objects (decrease divesity) Http:// .ugr.es

  9. Evolvability conditions • Objects can be evaluated, or at least, compared to each other, that is, they have a fitness or cost. 32 2 122 Http:// .ugr.es

  10. Evolving objects in practice • Program whatever you want to evolve in an object-oriented language (Smalltalk, Java, C++). • Decide evolution paths: changes to that class of objects, and, if applicable, how to interchange information among objects. • Create a population of objects, evaluate, change and combine them, keeping, cloning or combining the best, and eliminating the worst. Http:// .ugr.es

  11. Genetic algorithm demo • This is an example of evolvable objects (http://geneura.ugr.es/%7ejmerelo/EO.html) • It´s Open Source. You can insert it in yor presentations or do whatever you want with it. If you want, leave the footnote so that other people can also download it. If you make any interesting modification, please tell me. • It´s a simple GA that evolves character strings, using mutation (which adds or substracts an integer to the ASCII value) and 2-point crossover. It uses 2-tournament selection, and fitness is the total ASCII distance to the correct string. • Since it´s written in Visual Basic for Applications, it can also be used from Word or Excel. I´d be interested in hearing any development in this direction. • To run it, you have to Show this presentation. Http:// .ugr.es

  12. Mutation and Crossover Http:// .ugr.es

  13. Example: Genetic algorithm in VB for applications Search String Mutation prob. Population xOver prob. Generation # Http:// .ugr.es

  14. What´s new about EO? • Main thing are interfaces, through which objects can evolve. • Evolution is paradigm-independent. • Evolution is representation-independent. • Evolution is language-independent. • Any object can be evolved: programs, graphics, neural nets, mastermind solutions. Http:// .ugr.es

  15. Objects are everywhere! • Microsoft´s Common Object Model (COM). • Web Document Object Model (DOM). • Object Management Group’s CORBA, used in GNOME. • Java applets, IBM´s aglets, JINI. Http:// .ugr.es

  16. Meet EO http://geneura.ugr.es/~jmerelo/EO.html • C++ class library for evolving objects • EOs are chromosomes. • EO-level operator, generic or representation-specific. • Population-level operators, which are representation-independent. • Any evolutionary computation paradigm, or none at all, can be used. Http:// .ugr.es

  17. EO: status • General GA, simple GA, Evolution Strategies, Simulated annealing. • Many different EOs: bitstring, string, float vector, neural nets. • Applications: evolved neural nets, play mastermind. • Multiplatform: Win, Linux, Unix Http:// .ugr.es

  18. EO Roadmap • More language support: Java, Jini, Objective C • ActiveX-control, integration with COM • Graphic interface: Qt or GTK+. • Integration with GNOME? Volunteers needed! Http:// .ugr.es

  19. Neural nets: this is their life. • What neural nets ain´t. • What neural nets are. • How do they work. • What they don´t do. • What they do. • Applications/Case studies       Http:// .ugr.es

  20. What neural nets ain´t • A model of the brain. • A way to make machines intelligent. • Massively parallel and fault-tolerant. • Black boxes that can be used with no prior knowledge. Http:// .ugr.es

  21. What neural nets are • A new way to visualize adaptive statistical data analysis tools. • Real time signal processors, in hardware or in software. • A marketing term that has attracted lots of researchers to the realm of data analysis. • Local error minimization and search procedures: they find only local minima. Http:// .ugr.es

  22. NNets: what do they look like • Inputs: data taken from the environment or a file. • Weights: Links from inputs to computing elements. • Neurons: computing elements that apply a function f to the product of their inputs times the incoming weights. The result is called activation • Output: sometimes, activations are projected by applying a linear transformation. Http:// .ugr.es

  23. NNets: how do they work • After the training phase, NNs are used or exploited: weights are fixed, and they don´t learn any more. • Basically, they associate inputs to outputs or to other inputs. Http:// .ugr.es

  24. NNets: their applications • Classifiers: input = feature vector, output = classification value. Example: Quality control: good or faulty. • Associative memories: input = vector, output= vector. Retrieve image from a part of it. • Clustering methods: input = vector, output =closest vector to that one. Market segmentation: find “clusters” of similar customers. • Function approximators: input = vector, output= vector. Forecasting. Http:// .ugr.es

  25. What they don´t do • Always work. • Train fast. • Work in real-life problems. Http:// .ugr.es

  26. What they do • They always work. At least, most of the papers published show results (usually on toy problems). • They are fast. Or at least, they will be when they are ported to a hardware accelerator. • Work in real life problems. There are many commercial applications of neural networks, from forecasting, to pattern recognition, to real-time control. Http:// .ugr.es

  27. Some applications • NNs as models. But other networks are more appropiate for this: autocatalytic networks, for instance. • NNs as visualization tools: • Feature extraction  projection of n-dimensional spaces • NNs as data mining tools: creating bottom-up knowledge from data. • NNs as the “decision making” part of agents in multiagent models. Http:// .ugr.es

  28. Case study: Consumer/ad interaction no yes yes yes no Http:// .ugr.es

  29. Case Study: Consumer/ad interaction • Scenario: commodity product, of which any amount can be purchased (beer, tobacco, or any other vice). • No difference in performance or price. For instance, Coke/Pepsi. • Advertising works: ads reach their target, and they promote the sale of the product they are intended to promote. Http:// .ugr.es

  30. no no no Consumer/ad interaction • There are many agents, each one with a neural net . • Consumer deciding pro ad are rewarded. Those deciding anti-ad are punished. • Reward/punishment depend on investment on ads. yes Http:// .ugr.es

  31. Consumer/ad interaction: results • The model reflects very well (qualitatively) well known facts about advertising: • Pulsing strategies work better than continuous ones. • Introducing a new product in the market costs much more than keeping market share. • Total sales of a product segment can decrease due to bad publicity decisions. For instance, if all firms in a market segment invest more or less the same: coopetition. Http:// .ugr.es

  32. Consumer/ad interaction Http:// .ugr.es

  33. Information filtering • Customizing information provided to the user needs and preferences. • Documents are represented as “vectors” • Consumer preferences are sets of vectors, and adapted using a NN. Http:// .ugr.es

  34. Evolving neural nets • Evolving NNets solves the “model selection problem”: • Initial weights, neural net size and learning constant can be evolved. • Combines local search with global search. • NNet training can be an operator used to evolve. • G-LVQ: evolving LVQ, G-PROP: evolving BackProp. Http:// .ugr.es