Nature Inspired Computation and Applications Laboratory (NICAL), School of Computer Science and Technology, University of Science and Technology of China, Hefei, China.
Evol Comput. 2012 Fall;20(3):453-79. doi: 10.1162/EVCO_a_00054. Epub 2012 Jan 30.
This paper presents a comparative study of two indirect solution representations, a generative and an ontogenic one, on a set of well-known 2D truss design problems. The generative representation encodes the parameters of a trusses design as a mapping from a 2D space. The ontogenic representation encodes truss design parameters as a local truss transformation iterated several times, starting from a trivial initial truss. Both representations are tested with a naive evolution strategy based optimization scheme, as well as the state of the art HyperNEAT approach. We focus both on the best objective value obtained and the computational cost to reach a given level of optimality. The study shows that the two solution representations behave very differently. For experimental settings with equal complexity, with the same optimization scheme and settings, the generative representation provides results which are far from optimal, whereas the ontogenic representation delivers near-optimal solutions. The ontogenic representation is also much less computationally expensive than a direct representation until very close to the global optimum. The study questions the scalability of the generative representations, while the results for the ontogenic representation display much better scalability.
本文对两种间接解表示方法进行了比较研究，一种是生成式的，另一种是发生式的，针对一组著名的二维桁架设计问题。生成式表示将桁架设计的参数编码为从二维空间到映射。发生式表示将桁架设计参数编码为从一个简单的初始桁架迭代多次的局部桁架变换。这两种表示都使用基于自然进化策略的优化方案以及最先进的 HyperNEAT 方法进行了测试。我们既关注获得的最佳目标值，也关注达到给定优化水平的计算成本。研究表明，这两种解表示方法的行为非常不同。在具有相同复杂度的实验设置中，使用相同的优化方案和设置，生成式表示方法提供的结果远非最优，而发生式表示方法则提供了接近最优的解决方案。发生式表示方法的计算成本也远低于直接表示方法，直到非常接近全局最优。该研究对生成式表示方法的可扩展性提出了质疑，而发生式表示方法的结果则显示出更好的可扩展性。
