Extending Program Synthesis Grammars for Grammar-Guided Genetic Programming

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

doi:10.1007/978-3-319-99253-2_16

Cited by 26 publications

(23 citation statements)

References 17 publications

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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%

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: Benchmark Problemsmentioning

confidence: 99%

Lexicase selection of specialists

Helmuth

Pantridge²,

Spector

2019

Proceedings of the Genetic and Evolutionary Computation Conference

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“…PushGP and G3P use manually curated instruction sets and grammars that are designed not to "cheat" at solving the benchmarks with instructions that are too close to a solution. This practice is useful when comparing methods, but is unrepresentative of real-world applications, for 3 A listing of python functions used in our CBGP prototype can be found in the source code files at https://github.com/erp12/CodeBuildingGeneticProgramming-ProtoType/tree/master/push4/library which any augmentation or restriction of the instruction set would be acceptable if it produces results that meet a current need.…”

Section: Resultsmentioning

confidence: 99%

“…It requires the manual creation and integration of additional grammars [2]. It has also been shown that the performance of G3P is sensitive to the exact implementation of the grammars [3]. Similar to PushGP, we do not know of any prior work that attempts to extend G3P to support polymorphism.…”

Section: Related Workmentioning

confidence: 99%

“…Given a list of elements that are all of the same comparable type, T , and two instance of type T representing a lower and upper bound, filter the list to the elements that fall between two bounds (inclusively). For example, if given the list [6,5,4,3,2,1], the lower bound 3, and the upper bound 5 the result should be [5,4,3]. Also, given the list ["a","b","c"], the lower bound "x", and the upper bound "zzz" the result should be an empty list.…”

Section: Filter Boundsmentioning

confidence: 99%

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Code building genetic programming

Pantridge

Spector

2020

Proceedings of the 2020 Genetic and Evolutionary Computation Conference

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In recent years the field of genetic programming has made significant advances towards automatic programming. Research and development of contemporary program synthesis methods, such as PushGP and Grammar Guided Genetic Programming, can produce programs that solve problems typically assigned in introductory academic settings. These problems focus on a narrow, predetermined set of simple data structures, basic control flow patterns, and primitive, non-overlapping data types (without, for example, inheritance or composite types). Few, if any, genetic programming methods for program synthesis have convincingly demonstrated the capability of synthesizing programs that use arbitrary data types, data structures, and specifications that are drawn from existing codebases. In this paper, we introduce Code Building Genetic Programming (CBGP) as a framework within which this can be done, by leveraging programming language features such as reflection and first-class specifications. CBGP produces a computational graph that can be executed or translated into source code of a host language. To demonstrate the novel capabilities of CBGP, we present results on new benchmarks that use non-primitive, polymorphic data types as well as some standard program synthesis benchmarks.

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“…Often, especially in the older GP literature, domain-specific languages similar to S-Expressions are used [7], [8]. In recent work, realworld programming languages like C/C++ [9], Java [10], and above all Python [11], [12] are used with higher frequency. Nevertheless, also Push [13], a language specially designed for GP and not for practical software development, has been used regularly in recent years for program synthesis [14], [15].…”

Section: Introductionmentioning

confidence: 99%

Recent Developments in Program Synthesis with Evolutionary Algorithms

Sobania¹,

Schweim²,

Rothlauf³

2021

Preprint

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The automatic generation of computer programs is one of the main applications with practical relevance in the field of evolutionary computation. With program synthesis techniques not only software developers could be supported in their everyday work but even users without any programming knowledge could be empowered to automate repetitive tasks and implement their own new functionality. In recent years, many novel program synthesis approaches based on evolutionary algorithms have been proposed and evaluated on common benchmark problems. Therefore, we identify in this work the relevant evolutionary program synthesis approaches and provide an in-depth analysis of their performance. The most influential approaches we identify are stack-based, grammar-guided, as well as linear genetic programming. Further, we find that these approaches perform well on benchmark problems if there is a simple mapping from the given input to the correct output. On problems where this mapping is complex, e.g., if the problem consists of several subproblems or requires iteration/recursion for a correct solution, results tend to be worse. Consequently, for future work, we encourage researchers not only to use a program's output for assessing the quality of a solution but also the way towards a solution (e.g., correctly solved sub-problems).

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Extending Program Synthesis Grammars for Grammar-Guided Genetic Programming

Cited by 26 publications

References 17 publications

Lexicase selection of specialists

Lexicase selection of specialists

Code building genetic programming

Recent Developments in Program Synthesis with Evolutionary Algorithms

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