A Grammar Design Pattern for Arbitrary Program Synthesis Problems in Genetic Programming

Forstenlechner, Stefan; Fagan, David; Nicolau, Miguel; O’Neill, Michael

doi:10.1007/978-3-319-55696-3_17

Cited by 50 publications

(40 citation statements)

References 18 publications

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“…Figures 1 and 2 show the behavioral diversity of each selection method over the maximum 300 generations on two representative problems, Replace Space with Newline and Syllables. 3 The levels of diversity in these plots mirror those for other problems, which we do not present. Although novelty-lexicase selection did not achieve -a significantly better success rate than lexicase selection, the behavioral diversity of the novelty-lexicase populations was close to 1 for both problems, much higher than lexicase selection, which in turn is much higher than tournament selection.…”

Section: Gp System and Parameterssupporting

confidence: 59%

“…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%

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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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Section: Gp System and Parameterssupporting

confidence: 59%

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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“…and various control flow techniques. These problems have been addressed in several studies, using multiple genetic programming systems using lexicase selection including PushGP [7-10, 12, 14, 19, 20] and grammar guided GP [3][4][5][6], as well as at least one non-evolutionary program synthesis technique [25].…”

Section: Benchmark Problemsmentioning

confidence: 99%

“…Based on Equation 1, we can calculate how often we would expect tournament selection to select the individuals excluded by elitist survival. 3 The probabilities of tournament selection choosing an individual removed by elitist survival at different rates are given in Table 2. Tournament selection would select a decent proportion of the individuals removed by 30% elitist survival, at around 0.08.…”

Section: Importance Of Selecting Specialistsmentioning

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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“…The second set are previously established benchmarks (Helmuth and Spector 2015b), created partly in response to a community call for stronger benchmarks (McDermott et al 2012). These enable comparison with published results (Helmuth and Spector 2015b;Forstenlechner et al 2017;Pantridge et al 2017;Helmuth, McPhee, and Spector 2018;Forstenlechner et al 2018 Table 3: Benchmarks. In Time, n is total input size (may have multiple arrays & preallocated output space); add 1 to n before taking lg, add 1 to bound before truncating to integer.…”

Section: Benchmark Descriptionsmentioning

confidence: 99%

Stepping Stones to Inductive Synthesis of Low-Level Looping Programs

Rosin¹

2019

AAAI

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Inductive program synthesis, from input/output examples, can provide an opportunity to automatically create programs from scratch without presupposing the algorithmic form of the solution. For induction of general programs with loops (as opposed to loop-free programs, or synthesis for domain-specific languages), the state of the art is at the level of introductory programming assignments. Most problems that require algorithmic subtlety, such as fast sorting, have remained out of reach without the benefit of significant problem-specific background knowledge. A key challenge is to identify cues that are available to guide search towards correct looping programs. We present MAKESPEARE, a simple delayed-acceptance hillclimbing method that synthesizes low-level looping programs from input/output examples. During search, delayed acceptance bypasses small gains to identify significantly-improved stepping stone programs that tend to generalize and enable further progress. The method performs well on a set of established benchmarks, and succeeds on the previously unsolved "Collatz Numbers" program synthesis problem. Additional benchmarks include the problem of rapidly sorting integer arrays, in which we observe the emergence of comb sort (a Shell sort variant that is empirically fast). MAKESPEARE has also synthesized a record-setting program on one of the puzzles from the TIS-100 assembly language programming game.

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A Grammar Design Pattern for Arbitrary Program Synthesis Problems in Genetic Programming

Cited by 50 publications

References 18 publications

Comparing and combining lexicase selection and novelty search

Comparing and combining lexicase selection and novelty search

Lexicase selection of specialists

Stepping Stones to Inductive Synthesis of Low-Level Looping Programs

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