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

进化计算中的表示是存储问题的解决方案以供算法操作的方式。计算智能中一种常见的研究类型是调整参数，如种群大小、变异操作符的应用率和选择方法。最近在生物信息学、数学建模、博弈论、最优化和自动内容生成方面的工作表明，通过修改表示法可能获得的改进通常可以与通过参数调整获得的改进相媲美。这一建议试图在应用问题的背景下发明新的表示法，比较表示法之间的性能，从而检查不同表示法找到的解决方案的特征。这最后一个目标是最新颖的。副作用机器是我的团队发明的DNA分类表示法的一个例子。虽然基于副作用机器的特征通常表现良好，但通过将它们与不能由副作用机器编码的特征结合使用，可以获得更好的性能。我们发明的另一种可进化的表示，称为编织的强内核，它定位的特征不同于副作用机器中出现的特征，也是对这些特征的补充。在游戏研究中，我们小组的早期结果之一是，通过改变代理人的表征，可以在单一的实验背景下，对进化的囚徒困境代理人进行实验，以展示几乎任何所需的行为。在自动内容生成(用于视频游戏)中，我们发现不同的表示生成的级别地图具有非常不同的美学特征。这表明，拥有可用调色板的游戏设计师将有更大的设计空间可供探索。该小组还发明了用于真实参数优化的新表示法，这些表示法提高了性能或允许枚举最优。这些不同的主题被探索表征的主题统一起来。这一提议的结果应该是设计和选择进化计算的表示法的原则，以及解决问题的新技术，包括从工业合作者那里获得的应用问题。

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.