Lexicase selection outperforms previous strategies for incremental evolution of virtual creature controllers

Moore, Jared M.; Stanton, Adam

doi:10.7551/ecal_a_050

Cited by 22 publications

(29 citation statements)

References 15 publications

(21 reference statements)

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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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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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“…The animat, simulation environment, and wall-crossing task are continuations of previous work originally reported with respect to Lexicase selection in Moore and Stanton (2017). Specific design parameters are detailed in that work and its references.…”

Section: Methodsmentioning

confidence: 99%

“…If multiple individuals remain after all objectives have been used, a random selection is applied to the remaining individuals and recorded as a tiebreak event. For a full description of the Lexicase selection algorithm employed in this paper refer to Moore and Stanton (2017).…”

Section: Torso Dimensionmentioning

confidence: 99%

The Limits of Lexicase Selection in an Evolutionary Robotics Task

Moore

Stanton

2019

The 2019 Conference on Artificial Life

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Agents exhibiting generalized control are capable of solving a theme of related tasks, rather than a specific instance. Here, generalized control pertains to the locomotive capacity of quadrupedal animats, evaluated when climbing over walls of varying height to reach a target. In prior work, we showed that Lexicase selection is more effective than other evolutionary algorithms for this wall crossing task. Generalized controllers capable of crossing the majority of wall heights are discovered, even though Lexicase selection does not sample all possible environments per generation. In this work, we further constrain environmental sampling during evolution, examining the resilience of Lexicase to the impoverished conditions. Through restricting the range of samples at given points in time as well as fixing environmental exposure over fractions of evolutionary time, we attempt to increase the 'adjacency' of environmental samples, and report on the response of the Lexicase algorithm to the pressure of this reduced environmental diversity. Results indicate that Lexicase is robust, producing viable agents even in considerably challenging conditions. We also see a positive correlation between the number of tiebreak events that occur and the success of individuals in a population, except in the most limiting conditions. We argue that the increased number of tiebreaks is a response to local maxima, and the increased diversity resulting from random selection at this point, is a key driver of the resilience of the Lexicase algorithm. We also show that in extreme cases, this relationship breaks down. We conclude that tiebreaking is an important control mechanism in Lexicase operation, and that the breakdown in performance observed in extreme conditions indicates an inability of the tiebreak mechanism to function effectively where population diversity is unable to reflect environmental diversity.

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“…Previous studies have shown lexicase selection outperforming tournament selection and other parent selection methods in areas such as automatic program synthesis [3,13], boolean logic and finite algebras [12,14,25], evolutionary robotics [28], and boolean constraint satisfaction using genetic algorithms [27]. Additionally, ϵ-lexicase, a relaxed version of lexicase selection that at each step keeps any individuals within some threshold of the best individual, has performed well on symbolic regression problems [17,19].…”

Section: Lexicase Selectionmentioning

confidence: 99%

Comparing and combining lexicase selection and novelty search

Jundt

Helmuth

2019

Proceedings of the Genetic and Evolutionary Computation Conference

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Lexicase selection and novelty search, two parent selection methods used in evolutionary computation, emphasize exploring widely in the search space more than traditional methods such as tournament selection. However, lexicase selection is not explicitly driven to select for novelty in the population, and novelty search suffers from lack of direction toward a goal, especially in unconstrained, highly-dimensional spaces. We combine the strengths of lexicase selection and novelty search by creating a novelty score for each test case, and adding those novelty scores to the normal error values used in lexicase selection. We use this new novelty-lexicase selection to solve automatic program synthesis problems, and find it significantly outperforms both novelty search and lexicase selection. Additionally, we find that novelty search has very little success in the problem domain of program synthesis. We explore the effects of each of these methods on population diversity and long-term problem solving performance, and give evidence to support the hypothesis that novelty-lexicase selection resists converging to local optima better than lexicase selection.

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Lexicase selection outperforms previous strategies for incremental evolution of virtual creature controllers

Cited by 22 publications

References 15 publications

Lexicase selection of specialists

Lexicase selection of specialists

The Limits of Lexicase Selection in an Evolutionary Robotics Task

Comparing and combining lexicase selection and novelty search

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