Towards effective semantic operators for program synthesis in genetic programming

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

doi:10.1145/3205455.3205592

Cited by 13 publications

(12 citation statements)

References 13 publications

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“…More recently, Forstenlechner et al [8] showed how it is possible to compute the semantics of GP individuals for program synthesis. This operates on a range of di erent data types as opposed to those working on a single type of data.…”

Section: Related Work 21 Semantics In Genetic Programmingmentioning

confidence: 99%

Promoting semantic diversity in multi-objective genetic programming

Galván

Schoenauer

2019

Proceedings of the Genetic and Evolutionary Computation Conference

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The study of semantics in Genetic Programming (GP) has increased dramatically over the last years due to the fact that researchers tend to report a performance increase in GP when semantic diversity is promoted. However, the adoption of semantics in Evolutionary Multi-objective Optimisation (EMO), at large, and in Multiobjective GP (MOGP), in particular, has been very limited and this paper intends to ll this challenging research area. We propose a mechanism wherein a semantic-based distance is used instead of the widely known crowding distance and is also used as an objective to be optimised. To this end, we use two well-known EMO algorithms: NSGA-II and SPEA2. Results on highly unbalanced binary classi cation tasks indicate that the proposed approach produces more and better results than the rest of the three other approaches used in this work, including the canonical aforementioned EMO algorithms. CCS CONCEPTS• Computing methodologies → Genetic programming;

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Section: Related Work 21 Semantics In Genetic Programmingmentioning

confidence: 99%

Promoting semantic diversity in multi-objective genetic programming

Galván

Schoenauer

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%

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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“…Small or Large [16], [29], [74], [48], [39], [75], [76], [40], [42], [41], [38] [11], [61], [79], [12], [2], [63] [68] 18…”

Section: Benchmark Problem Stack-based Gp Grammar-guided Gp Linear Gp...mentioning

confidence: 99%

“…Compare String Lengths [16], [29], [74], [48], [39], [75], [51], [76], [52], [40], [53], [42], [41], [80], [45], [54], [38] [11], [61], [79], [78], [2], [63] [68] 24…”

Section: Benchmark Problem Stack-based Gp Grammar-guided Gp Linear Gp...mentioning

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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Towards effective semantic operators for program synthesis in genetic programming

Cited by 13 publications

References 13 publications

Promoting semantic diversity in multi-objective genetic programming

Promoting semantic diversity in multi-objective genetic programming

Lexicase selection of specialists

Recent Developments in Program Synthesis with Evolutionary Algorithms

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