论文标题
解决方案和健身进化（安全）：多物理问题的研究
Solution and Fitness Evolution (SAFE): A Study of Multiobjective Problems
论文作者
论文摘要
我们最近提出了安全的 - 解决方案和健身进化 - 一种相应的协调算法，该算法维持两个共同发展的人群：候选解决方案的群体和候选目标函数的群体。我们表明，安全在机器人迷宫领域内不断发展的解决方案。本文中，我们介绍了Safe的适应和对多目标问题的应用的调查，其中候选目标功能探讨了每个目标的不同权重。尽管初步表明，结果表明安全以及共同发展的解决方案和目标功能的概念可以识别一组类似的最佳多物镜解决方案，而无需明确使用帕累托前锋进行健身计算和父母选择。这些发现支持我们的假设，即安全算法概念不仅可以解决复杂的问题，而且可以适应多个目标问题的挑战。
We have recently presented SAFE -- Solution And Fitness Evolution -- a commensalistic coevolutionary algorithm that maintains two coevolving populations: a population of candidate solutions and a population of candidate objective functions. We showed that SAFE was successful at evolving solutions within a robotic maze domain. Herein we present an investigation of SAFE's adaptation and application to multiobjective problems, wherein candidate objective functions explore different weightings of each objective. Though preliminary, the results suggest that SAFE, and the concept of coevolving solutions and objective functions, can identify a similar set of optimal multiobjective solutions without explicitly employing a Pareto front for fitness calculation and parent selection. These findings support our hypothesis that the SAFE algorithm concept can not only solve complex problems, but can adapt to the challenge of problems with multiple objectives.
