Evolutionary algorithms oftenLineage age become stuck on a particular evolutionary trajectory, limiting the available outcomes and often excluding global optima. Indeed, the starting state of a population (or early decisions) may limit the regions of search space that the algorithm will ultimately explore. One mechanism used to ensure that more regions of a search space are considered is to regularly inject new random starting points, while giving special advantages to younger lineages to give them a chance to survive long enough to realize their potential. In this chapter, we explore including periodic injections of random solutions into lexicase selectionLexicase selection, along with age-based selection criteria. We demonstrate this technique’s potential for increased exploration using both program synthesisProgram synthesis benchmark problems and construction of SudokuSudoku boards with particular solving characteristics. Our results are promising, but inconsistent, ranging from highly effective to no meaningful difference from controls. Ultimately, we provide directions for future research that might more fully realize the power of age-based criteria in lexicase selectionLexicase selection.