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。计算智能中的一种常见研究类型是调整参数，如群体大小，变异算子的应用率和选择方法。最近在生物信息学、数学建模、博弈论、优化和自动内容生成方面的工作表明，通过修改表示可能取得的改进通常与通过参数调整获得的改进相比较好。这个建议试图在应用问题的上下文中发明新的表示，比较表示之间的性能，从而检查不同表示所发现的解决方案的特征。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分类代表的一个例子。虽然基于副作用机器的特征通常表现良好，但通过将它们与副作用机器无法编码的特征组合使用，可以获得更好的性能。我们发明的另一种可进化的表示，称为编织强内核，它定位的特征与副作用机器中出现的特征不同，并且是互补的。在游戏研究中，我们小组的早期成果之一是，通过改变代理人的表示，可以在单个实验环境中使进化的囚徒困境代理人的实验表现出几乎任何期望的行为。在自动内容生成（视频游戏）中，我们发现不同的表示方法生成的关卡地图具有非常不同的美学特征。这表明，一个拥有可用表示调色板的游戏设计师将有更大的设计空间来探索。该小组还发明了新的表示真实的参数优化，提高性能或允许枚举的最优。这些不同的主题被探索表征的主题统一起来。这个建议的结果应该是进化计算的设计和选择代表的原则，以及解决问题的新技术，包括从工业合作者获得的应用问题。

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.