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IMEC Conceptual Model

IMEC Conceptual Model. Matthew Rabiee Jonathan Russell Justin Smith. Abstract.

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IMEC Conceptual Model

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  1. IMEC Conceptual Model Matthew Rabiee Jonathan Russell Justin Smith

  2. Abstract • We are to design an application to interact with Dr. Dozier's genetic algorithm. This application is to be on individual machines that interact, through the internet, with a server. This server will have a Meme space and the genetic algorithm running on it. The application will display the non-dominating solutions to the scientists and allow them to select coordinates of the preferred solutions.

  3. Task Analysis • Select Individuals • Deselect Individuals • Sort Individuals by a Function • Unsort Individuals • Reorder Functions • Hide Functions • Unhide Functions • Submit Selected Individuals

  4. Lexicon • Genetic Algorithm -An evolutionary algorithm which generates each individual from some encoded form known as a "chromosome" or "genome". Chromosomes are combined or mutated to breed new individuals. "Crossover", the kind of recombination of chromosomes found in sexual reproduction in nature, is often also used in GAs. Here, an offspring's chromosome is created by joining segments choosen alternately from each of two parents' chromosomes which are of fixed length. • Meme Space - Memes are contagious ideas, all competing for a share of our mind in a kind of Darwinian selection. As memes evolve, they become better and better at distracting and diverting us from whatever we'd really like to be doing with our lives. They are a kind of Drug of the Mind. • Non-dominating Solution - A plan non-dominated if no other single plan has been found which is better in all objectives. • Pareto Front – A collection of non-dominating solutions.

  5. Task Scenario • A NASA scientist is needed to select non-dominated solutions that are generated by a Genetic Algorithm. He needs an application that will allow him to visualize the data to make better decisions on which solutions are preferred.

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