An Exploration of Exploration: Measuring the ability of lexicase selection to find obscure pathways to optimality
An Exploration of Exploration: Measuring the ability of lexicase selection to find obscure pathways to optimality
复制标题
探索中的探索:衡量词汇选择的能力,以找到通往最优性的模糊途径
DOI:
10.1007/978-981-16-8113-4_5
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发表时间:
2021
期刊:
影响因子:
--
通讯作者:
Charles Ofria
中科院分区:
文献类型:
--
作者:
Jose Guadalupe Hernandez;Alexander Lalejini;Charles Ofria
Parent selection algorithms (selection schemes) steer populations through a problem's search space, often trading off between exploitation and exploration. Understanding how selection schemes affect exploitation and exploration within a search space is crucial to tackling increasingly challenging problems. Here, we introduce an"exploration diagnostic"that diagnoses a selection scheme's capacity for search space exploration. We use our exploration diagnostic to investigate the exploratory capacity of lexicase selection and several of its variants: epsilon lexicase, down-sampled lexicase, cohort lexicase, and novelty-lexicase. We verify that lexicase selection out-explores tournament selection, and we show that lexicase selection's exploratory capacity can be sensitive to the ratio between population size and the number of test cases used for evaluating candidate solutions. Additionally, we find that relaxing lexicase's elitism with epsilon lexicase can further improve exploration. Both down-sampling and cohort lexicase -- two techniques for applying random subsampling to test cases -- degrade lexicase's exploratory capacity; however, we find that cohort partitioning better preserves lexicase's exploratory capacity than down-sampling. Finally, we find evidence that novelty-lexicase's addition of novelty test cases can degrade lexicase's capacity for exploration. Overall, our findings provide hypotheses for further exploration and actionable insights and recommendations for using lexicase selection. Additionally, this work demonstrates the value of selection scheme diagnostics as a complement to more conventional benchmarking approaches to selection scheme analysis.
DOI:
10.1145/3319619.3326900
发表时间:
2019
期刊:
GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference
影响因子:
--
作者:
Hernandez, Jose Guadalupe;Lalejini, Alexander;Dolson, Emily;Ofria, Charles
通讯作者:
Ofria, Charles
影响因子:
2.6
作者:
Dolson, Emily;Lalejini, Alexander;Jorgensen, Steven;Ofria, Charles
通讯作者:
Ofria, Charles