9,745 research outputs found
A multi-population hybrid Genetic Programming System
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsIn the last few years, geometric semantic genetic programming has incremented
its popularity, obtaining interesting results on several real life applications. Nevertheless,
the large size of the solutions generated by geometric semantic genetic
programming is still an issue, in particular for those applications in which reading
and interpreting the final solution is desirable. In this thesis, a new parallel
and distributed genetic programming system is introduced with the objective of
mitigating this drawback. The proposed system (called MPHGP, which stands for
Multi-Population Hybrid Genetic Programming) is composed by two types of subpopulations,
one of which runs geometric semantic genetic programming, while
the other runs a standard multi-objective genetic programming algorithm that optimizes,
at the same time, fitness and size of solutions. The two subpopulations
evolve independently and in parallel, exchanging individuals at prefixed synchronization
instants. The presented experimental results, obtained on five real-life
symbolic regression applications, suggest that MPHGP is able to find solutions
that are comparable, or even better, than the ones found by geometric semantic
genetic programming, both on training and on unseen testing data. At the same
time, MPHGP is also able to find solutions that are significantly smaller than the
ones found by geometric semantic genetic programming
How Noisy Data Affects Geometric Semantic Genetic Programming
Noise is a consequence of acquiring and pre-processing data from the
environment, and shows fluctuations from different sources---e.g., from
sensors, signal processing technology or even human error. As a machine
learning technique, Genetic Programming (GP) is not immune to this problem,
which the field has frequently addressed. Recently, Geometric Semantic Genetic
Programming (GSGP), a semantic-aware branch of GP, has shown robustness and
high generalization capability. Researchers believe these characteristics may
be associated with a lower sensibility to noisy data. However, there is no
systematic study on this matter. This paper performs a deep analysis of the
GSGP performance over the presence of noise. Using 15 synthetic datasets where
noise can be controlled, we added different ratios of noise to the data and
compared the results obtained with those of a canonical GP. The results show
that, as we increase the percentage of noisy instances, the generalization
performance degradation is more pronounced in GSGP than GP. However, in
general, GSGP is more robust to noise than GP in the presence of up to 10% of
noise, and presents no statistical difference for values higher than that in
the test bed.Comment: 8 pages, In proceedings of Genetic and Evolutionary Computation
Conference (GECCO 2017), Berlin, German
Sequential Symbolic Regression with Genetic Programming
This chapter describes the Sequential Symbolic Regression (SSR) method, a new strategy for function approximation in symbolic regression. The SSR method is inspired by the sequential covering strategy from machine learning, but instead of sequentially reducing the size of the
problem being solved, it sequentially transforms the original problem into potentially simpler problems. This transformation is performed according to the semantic distances between the desired and obtained outputs and a geometric semantic operator. The rationale behind SSR is that, after generating a suboptimal function f via symbolic regression, the output errors can be approximated by another function in a subsequent iteration. The method was tested in eight polynomial functions, and compared with canonical genetic programming (GP) and geometric semantic genetic programming (SGP). Results showed that SSR significantly outperforms SGP and presents no statistical difference to GP. More importantly, they show the potential of the proposed strategy: an effective way of applying geometric semantic operators to combine different (partial) solutions, avoiding the exponential growth problem arising from the use of these operators
Geometric Semantic Genetic Programming
Traditional Genetic Programming (GP) searches the space of functions/programs by using search operators that manipulate their syntactic representation, regardless of their actual semantics/behaviour. Recently, semantically aware search operators have been shown to outperform purely syntactic operators. In this work, using a formal geometric view on search operators and representations, we bring the semantic approach to its extreme consequences and introduce a novel form of GP – Geometric Semantic GP (GSGP) – that searches directly the space of the underlying semantics of the programs. This perspective provides new insights on the relation between program syntax and semantics, search operators and fitness landscape, and allows for principled formal design of semantic search operators for different classes of problems. We de- rive specific forms of GSGP for a number of classic GP domains and experimentally demonstrate their superiority to conventional operators
Geometric Semantic Genetic Programming
Tato práce se zabĂ˝vá pĹ™evodem Ĺ™ešenĂ zĂskanĂ©ho geometrickĂ˝m sĂ©mantickĂ˝m genetickĂ˝m programovánĂm (GSGP) na instanci kartĂ©zskĂ©ho genetickĂ©ho programovánĂ (CGP). GSGP se ukázalo jakoĹľto kvalitnĂ pĹ™i tvorbÄ› sloĹľitĂ˝ch matematickĂ˝ch modelĹŻ, ale problĂ©mem je vĂ˝sledná velikost Ĺ™ešenĂ. CGP zase dokáže dobĹ™e redukovat velikost jiĹľ vzniklĂ˝ch Ĺ™ešenĂ. Tato práce dala pomocĂ kombinacĂ tÄ›chto dvou metod vzniknout podstromovĂ©mu CGP (SCGP), kterĂ© jako vstup pouĹľĂvá vĂ˝stup GSGP a evoluci pak provádĂ pomocĂ CGP. Experimenty provedenĂ© na ÄŤtyĹ™ech Ăşlohách z oblasti farmakokinetiky ukázaly, Ĺľe SCGP dokáže vĹľdy zmenšit Ĺ™ešenĂ a ve tĹ™ech ze ÄŤtyĹ™ pĹ™ĂpadĹŻ navĂc ĂşspěšnÄ› bez pĹ™etrĂ©novánĂ.This thesis examines a conversion of a solution produced by geometric semantic genetic programming (GSGP) to an instantion of cartesian genetic programming (CGP). GSGP has proven its quality to create complex mathematical models; however, the size of these models can get problematically large. CGP, on the other hand, is able to reduce the size of given models. This thesis combinated these methods to create a subtree CGP (SCGP). The SCGP uses an output of GSGP as an input and the evolution is performed using the CGP. Experiments performed on four pharmacokinetic tasks have shown that the SCGP is able to reduce the solution size in every case. Overfitting was detected in one out of four test problems.
Geometric Semantic Genetic Programming
Traditional Genetic Programming (GP) searches the space of functions/programs by using search operators that manipulate their syntactic representation, regardless of their actual semantics/behaviour. Recently, semantically aware search operators have been shown to outperform purely syntactic operators. In this work, using a formal geometric view on search operators and representations, we bring the semantic approach to its extreme consequences and introduce a novel form of GP – Geometric Semantic GP (GSGP) – that searches directly the space of the underlying semantics of the programs. This perspective provides new insights on the relation between program syntax and semantics, search operators and fitness landscape, and allows for principled formal design of semantic search operators for different classes of problems. We de- rive specific forms of GSGP for a number of classic GP domains and experimentally demonstrate their superiority to conventional operators
Geometric Semantic Grammatical Evolution
This is the author accepted manuscript. The final version is available from Springer via the DOI in this record.Geometric Semantic Genetic Programming (GSGP) is a novel form of
Genetic Programming (GP), based on a geometric theory of evolutionary algorithms,
which directly searches the semantic space of programs. In this chapter,
we extend this framework to Grammatical Evolution (GE) and refer to the new
method as Geometric Semantic Grammatical Evolution (GSGE). We formally derive
new mutation and crossover operators for GE which are guaranteed to see a simple
unimodal fitness landscape. This surprising result shows that the GE genotypephenotype
mapping does not necessarily imply low genotype-fitness locality. To
complement the theory, we present extensive experimental results on three standard
domains (Boolean, Arithmetic and Classifier)
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