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.
翻译：我们最近介绍了SAFE -- -- 解决方案和适合性进化 -- -- 一种共通性共进演进算法,它维持着两种不断变化的人口:一组是候选解决方案,一组是候选客观功能。我们表明,SAFE成功地在机器人迷宫范围内开发了解决方案。我们在这里对SAFE的适应和应用于多目标问题进行了调查,其中候选人的客观功能探索了每个目标的不同权重。虽然初步的结果表明,SAFE和共同演变解决方案和客观功能的概念可以确定一套类似的最佳多目标解决方案,而不必明确使用Pareto来进行健身计算和父母选择。这些结论支持我们的假设,即SAFE算法概念不仅能够解决复杂的问题,而且能够适应多重目标问题的挑战。
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