Lexicase Selection Beyond Genetic Programming

Metevier, Blossom; Saini, Anil; Spector, Lee

doi:10.1007/978-3-030-04735-1_7

Cited by 30 publications

(13 citation statements)

References 7 publications

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“…After randomly shuffling the training cases, lexicase selection goes through them one by one, removing any individuals that do not give the best performance on each case until either a single individual or a single case remains. Lexicase selection has produced better performance than other parent selection methods in a variety of evolutionary computation systems and problem domains (Aenugu & Spector, 2019;Forstenlechner et al, 2017;La Cava et al, 2019;Liskowski et al, 2015;Oksanen & Hu, 2017;Orzechowski et al, 2018;Metevier et al, 2019;Moore & Stanton, 2017, 2019. Hernandez et al (2019) introduced down-sampled lexicase selection, a variant of lexicase selection that was developed further by Ferguson et al (2019).…”

Section: Related Workmentioning

confidence: 99%

Problem-Solving Benefits of Down-Sampled Lexicase Selection

Helmuth

Spector

2021

Artificial Life

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In genetic programming, an evolutionary method for producing computer programs that solve specified computational problems, parent selection is ordinarily based on aggregate measures of performance across an entire training set. Lexicase selection, by contrast, selects on the basis of performance on random sequences of training cases; this has been shown to enhance problem-solving power in many circumstances. Lexicase selection can also be seen as better reflecting biological evolution, by modeling sequences of challenges that organisms face over their lifetimes. Recent work has demonstrated that the advantages of lexicase selection can be amplified by down-sampling, meaning that only a random subsample of the training cases is used each generation. This can be seen as modeling the fact that individual organisms encounter only subsets of the possible environments and that environments change over time. Here we provide the most extensive benchmarking of down-sampled lexicase selection to date, showing that its benefits hold up to increased scrutiny. The reasons that down-sampling helps, however, are not yet fully understood. Hypotheses include that down-sampling allows for more generations to be processed with the same budget of program evaluations; that the variation of training data across generations acts as a changing environment, encouraging adaptation; or that it reduces overfitting, leading to more general solutions. We systematically evaluate these hypotheses, finding evidence against all three, and instead draw the conclusion that down-sampled lexicase selection's main benefit stems from the fact that it allows the evolutionary process to examine more individuals within the same computational budget, even though each individual is examined less completely.

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Section: Related Workmentioning

confidence: 99%

Problem-Solving Benefits of Down-Sampled Lexicase Selection

Helmuth

Spector

2021

Artificial Life

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“…Among the most significant of these variations is epsilon lexicase selection, in which "exactly the lowest error" in the description of the algorithm is replaced with "within epsilon of the lowest error" for a suitably defined epsilon; this has proven to be particularly effective on problems with floating-point errors [16,17]. Additionally, lexicase selection has been effectively used to solve problems in areas such as boolean logic and finite algebras [11,13,18], evolutionary robotics [22], and boolean constraint satisfaction using genetic algorithms [21].…”

Section: Background On Lexicase Selectionmentioning

confidence: 99%

Lexicase selection of specialists

Helmuth

Pantridge²,

Spector

2019

Proceedings of the Genetic and Evolutionary Computation Conference

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Lexicase parent selection filters the population by considering one random training case at a time, eliminating any individuals with errors for the current case that are worse than the best error in the selection pool, until a single individual remains. This process often stops before considering all training cases, meaning that it will ignore the error values on any cases that were not yet considered. Lexicase selection can therefore select specialist individuals that have poor errors on some training cases, if they have great errors on others and those errors come near the start of the random list of cases used for the parent selection event in question. We hypothesize here that selecting these specialists, which may have poor total error, plays an important role in lexicase selection's observed performance advantages over error-aggregating parent selection methods such as tournament selection, which select specialists much less frequently. We conduct experiments examining this hypothesis, and find that lexicase selection's performance and diversity maintenance degrade when we deprive it of the ability of selecting specialists. These findings help explain the improved performance of lexicase selection compared to tournament selection, and suggest that specialists help drive evolution under lexicase selection toward global solutions.

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“…Lexicase selection is designed to work in situations where fitness can be broken down in to multiple constituent parts (Spector, 2012). Conventionally, these constituent parts, or fitness criteria, are individual test cases in a genetic programming problem, but lexicase selection has been shown to work well in other scenarios too (Dolson et al, 2018a;Metevier et al, 2019). To select a parent with lexicase selection, the fitness criteria are randomly ordered.…”

Section: Lexicase Selectionmentioning

confidence: 99%

What can phylogenetic metrics tell us about useful diversity in evolutionary algorithms?

Hernandez¹,

Lalejini²,

Dolson³

2021

Preprint

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It is generally accepted that "diversity" is associated with success in evolutionary algorithms. However, diversity is a broad concept that can be measured and defined in a multitude of ways. To date, most evolutionary computation research has measured diversity using the richness and/or evenness of a particular genotypic or phenotypic property. While these metrics are informative, we hypothesize that other diversity metrics are more strongly predictive of success. Phylogenetic diversity metrics are a class of metrics popularly used in biology, which take into account the evolutionary history of a population. Here, we investigate the extent to which 1) these metrics provide different information than those traditionally used in evolutionary computation, and 2) these metrics better predict the long-term success of a run of evolutionary computation. We find that, in most cases, phylogenetic metrics behave meaningfully differently from other diversity metrics. Moreover, our results suggest that phylogenetic diversity is indeed a better predictor of success.

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Lexicase Selection Beyond Genetic Programming

Cited by 30 publications

References 7 publications

Problem-Solving Benefits of Down-Sampled Lexicase Selection

Problem-Solving Benefits of Down-Sampled Lexicase Selection

Lexicase selection of specialists

What can phylogenetic metrics tell us about useful diversity in evolutionary algorithms?

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