Semantic Backpropagation for Designing Search Operators in Genetic Programming

Pawlak, Tomasz P.; Wieloch, Bartosz; Krawiec, Krzysztof

doi:10.1109/tevc.2014.2321259

Cited by 79 publications

(66 citation statements)

References 24 publications

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“…Among the formal frameworks known in computer science, certain variants of operational semantics (so-called big-step or natural semantics) conform to this definition. As it will become clear in the following, the particular formalization of semantics used in the past GP work [5,10,13,21,25,29,33,38] and adopted in this study meets the above requirement as well.…”

Section: Program Semantics In Genetic Programmingmentioning

confidence: 91%

“…We adopt this assumption for consistency with most of the earlier works on semantic GP [2,13,21,25,33,38,39], and also to ease the understanding of the properties of semantic space. However, as it will become clear in the further reading, none of the crossover operators considered here requires explicit access to the desired outputs.…”

Section: Program Semantics In Genetic Programmingmentioning

confidence: 99%

“…The contributions in GSGP crossovers are scattered in multiple papers [21,24,25,29,33], where they have been presented using different formal backgrounds, tested in various configurations and different benchmark suites, with not necessarily identical parameter settings and software implementations that may have varied in details. The recently published survey [40] neatly categorizes the semantic methods, but does not provide a unified formal framework nor does it empirically compare the methods.…”

Section: Introductionmentioning

confidence: 99%

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Review and comparative analysis of geometric semantic crossovers

Pawlak

Wieloch

Krawiec

2014

Genet Program Evolvable Mach

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This paper provides a structured, unified, formal and empirical perspective on all geometric semantic crossover operators proposed so far, including the exact geometric crossover by Moraglio, Krawiec, and Johnson, as well as the approximately geometric operators. We start with presenting the theory of geometric semantic genetic programming, and discuss the implications of geometric operators for the structure of fitness landscape. We prove that geometric semantic crossover can by construction produce an offspring that is not worse than the fitter parent, and that under certain conditions such an offspring is guaranteed to be not worse than the worse parent. We review all geometric semantic crossover operators presented to date in the literature, and conduct a comprehensive experimental comparison using a tree-based genetic programming framework and a representative suite of nine symbolic regression and nine Boolean function synthesis tasks. We scrutinize the performance (program error and success rate), generalization, computational cost, bloat, population diversity, and the operators' capability to generate geometric offspring. The experiment leads to several interesting conclusions, the primary one being that an operator's capability to produce geometric offspring is positively correlated with performance. The paper is concluded by recommendations regarding the suitability of operators for the particular domains of program induction tasks.

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Section: Program Semantics In Genetic Programmingmentioning

confidence: 91%

Section: Program Semantics In Genetic Programmingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Review and comparative analysis of geometric semantic crossovers

Pawlak

Wieloch

Krawiec

2014

Genet Program Evolvable Mach

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“…By nature, program semantics is more detailed than the scalar fitness commonly used in GP, and in effect offers a more informative guidance for program synthesis. This virtue of SGP has brought substantial performance gains in numerous works [1,3,13,19,23,27,30,31].…”

Section: Introductionmentioning

confidence: 99%

“…This simple formalism, known as sampling semantics or simply semantics, provides a relatively detailed account on program execution at no additional computational cost. Previous studies have shown that, even though sampling semantics cannot convey information on all aspects of program execution, it can be leveraged to design efficient semantic-aware search operators [8,13,23,26,27,30,31], population initialization [2,7] and selection techniques [6].…”

Section: Introductionmentioning

confidence: 99%

Progress properties and fitness bounds for geometric semantic search operators

Pawlak

Krawiec

2015

Genet Program Evolvable Mach

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Metrics are essential for geometric semantic genetic programming. On one hand, they structure the semantic space and govern the behavior of geometric search operators; on the other, they determine how fitness is calculated. The interactions between these two types of metrics are an important aspect that to date was largely neglected. In this paper, we investigate these interactions and analyze their consequences. We provide a systematic theoretical analysis of the properties of abstract geometric semantic search operators under Minkowski metrics of arbitrary order. For nine combinations of popular metrics (city-block, Euclidean, and Chebyshev) used in fitness functions and of search operators, we derive pessimistic bounds on fitness change. We also define three types of progress properties (weak, potential, and strong) and verify them for operators under those metrics. The analysis allows us to determine the combinations of metrics that are most attractive in terms of progress properties and deterioration bounds.

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Preliminaries—Evolutionary Algorithms

Khandelwal

2022

Automating Data-Driven Modelling of Dynamical Systems

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Semantic Backpropagation for Designing Search Operators in Genetic Programming

Cited by 79 publications

References 24 publications

Review and comparative analysis of geometric semantic crossovers

Review and comparative analysis of geometric semantic crossovers

Progress properties and fitness bounds for geometric semantic search operators

Preliminaries—Evolutionary Algorithms

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