Representation and Applications of Evolutionary Computation to Bioinformatics, Optimization, and Games.

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Representation in evolutionary computation is the way that solutions to a problem are stored for manipulation by the algorithm. A common type of research in computational intelligence is to tune parameters such as population size, rate of application of variation operators, and selection method. Recent work in bioinformatics, mathematical modeling, game theory, optimization, and automatic content generation has shown that the improvement made possibly by modifying representation is often compares well with that obtained by parameter tuning. This proposal seeks to invent new representations in the context of applied problems, compare performance between representations, and so examine the character of solution found by different representations. This last goal is the most novel. Side effect machines are an example of a representation invented by my group for DNA classification. While side-effect machine based features often perform well, even better performance was obtained by using them in combination with features that cannot be encoded by a side effect machine. Another evolvable representation we invented, called a woven strong kernel locates features that are different from, and complimentary to, those arising in side effect machines. In games research, one of our group's early results is that experiments with evolving prisoner's dilemma agents can be made to exhibit almost any desired behavior within a single experimental context by changing the agent's representation. In automatic content generation (for video games) we found that different representations generated level maps with very different aesthetic characters. This suggests that a game designer with a palette of representations available will have a larger design space to explore. The group has also invented new representations for real parameter optimization which improve performance or permit the enumeration of optima. These diverse topics are unified by the theme of exploring representation. The result of this proposal should be principles for the design and selection of representation for evolutionary computation as well as novel techniques for problem solving, including applied problems obtained from industrial collaborators.