A Comparative Study of Different Grammar-Based Genetic Programming Approaches

Lourenço, Nuno; Ferrer, Joaquim; Pereira, Francisco B.; Costa, Ernesto

doi:10.1007/978-3-319-55696-3_20

Cited by 19 publications

(30 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…GE is like any other search algorithm, and in fact like any tool: it has its advantages and disadvantages, and will only provide its best performance if correctly used. There has been a recent surge of publications criticising GE's performance [42,18,21,17], some even deeming that its performance "resembles that of random search" [42]. But most of the results provided were the result of using badly designed grammars, and poor experimental setup.…”

Section: Discussionmentioning

confidence: 99%

“…See for example the history of attempts at solving the Santa Fe Ant Trail Problem [15] with GE, from the original incorrect grammar [29], to a first [35] and then second [9] correction, and its analysis [27]. And yet, recent publications [42,18,17,21] still use incorrect grammars, not respecting the original problem syntax.…”

Section: Balanced Grammarsmentioning

confidence: 99%

“…Populations were initialised using Random Initialisation (RND) (random integer strings), or using a depth-less variant of Probabilistic Tree Creation 2 (PTC2) [19,23]. These were chosen as many recent publications still use RND initialisation [17,21], whereas PTC2 was chosen for its proven performance [23]. Depending on the grammar used, the specified number of codons/expansions for initialisation is sufficient to generate expressions of up to a corresponding syntax tree depth of 5.…”

Section: Transformations Analysismentioning

confidence: 99%

See 2 more Smart Citations

Understanding Grammatical Evolution: Grammar Design

Nicolau

Agapitos

2018

Handbook of Grammatical Evolution

View full text Add to dashboard Cite

A frequently overlooked consideration when using Grammatical Evolution (GE) is grammar design. This is because there is an infinite number of grammars that can specify the same syntax. There are, however, certain aspects of grammar design that greatly affect the speed of convergence and quality of solutions generated with GE. In this chapter, general guidelines for grammar design are presented. These are domain-independent, and can be used when applying GE to any problem. An extensive analysis of their effect and results across a large set of experiments are reported.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Balanced Grammarsmentioning

confidence: 99%

Section: Transformations Analysismentioning

confidence: 99%

See 1 more Smart Citation

Understanding Grammatical Evolution: Grammar Design

Nicolau

Agapitos

2018

Handbook of Grammatical Evolution

View full text Add to dashboard Cite

show abstract

“…In the past, GE has been successfully used to build models for glucose prediction in diabetic patients [7], [8]. Besides, in [12], Lourenço et al showed that SGE has a better performance than GE on several different problems. These works motivated us to compare the performance of SGE with GE in the problem of glucose modeling, a real-world problem that will be described in this section.…”

Section: Glucose Prediction: a Real World Problemmentioning

confidence: 99%

Structured grammatical evolution for glucose prediction in diabetic patients

Lourenço

Colmenar

Hidalgo

et al. 2019

Proceedings of the Genetic and Evolutionary Computation Conference

Self Cite

View full text Add to dashboard Cite

Structured grammatical evolution is a recent grammar-based genetic programming variant that tackles the main drawbacks of Grammatical Evolution, by relying on a one-to-one mapping between each gene and a non-terminal symbol of the grammar. It was applied, with success, in previous works with a set of classical benchmarks problems. However, assessing performance on hard real-world problems is still missing. In this paper, we fill in this gap, by analyzing the performance of SGE when generating predictive models for the glucose levels of diabetic patients. Our algorithm uses features that take into account the past glucose values, insulin injections, and the amount of carbohydrate ingested by a patient. The results show that SGE can evolve models that can predict the glucose more accurately when compared with previous grammarbased approaches used for the same problem. Additionally, we also show that the models tend to be more robust, since the behavior in the training and test data is very similar, with a small variance. CCS CONCEPTS • Computing methodologies → Artificial intelligence; • Applied computing → Health informatics; • Theory of computation → Algorithm design techniques;

show abstract

“…This representation ensures that the modification of a gene does not affect the derivation of other non-terminals, thereby increasing locality [31]. SGE has been showed to be more effective than standard GE [32] and also to exhibit better locality and lower degeneracy [31]. A more recent study showed that the interaction of genotype size, crossover, and diversity may reduce the degree to which SGE satisfies these properties [28].…”

Section: Sgementioning

confidence: 99%

Weighted Hierarchical Grammatical Evolution

Bartoli

Castelli

Medvet

2020

IEEE Trans. Cybern.

View full text Add to dashboard Cite

Grammatical Evolution (GE) is one of the most widespread techniques in evolutionary computation. Genotypes in GE are bit strings while phenotypes are strings of a language defined by a user-provided context-free grammar (CFG). In this work, we propose a novel procedure for mapping genotypes to phenotypes that we call Weighted Hierarchical GE (WHGE). WHGE imposes a form of hierarchy on the genotype and encodes grammar symbols with a varying number of bits based on the relative expressive power of those symbols. WHGE does not impose any constraint on the overall GE framework, in particular, WHGE may handle recursive grammars, uses the classical genetic operators, and does not need to define any bound in advance on the size of phenotypes. We assessed experimentally our proposal in depth on a set of challenging and carefully selected benchmarks, comparing the results to the standard GE framework as well as to two of the most significant enhancements proposed in the literature: Position-independent GE and Structured GE. Our results show that WHGE delivers very good results in terms of fitness as well as in terms of the properties of the genotype-phenotype mapping procedure.

show abstract

A Comparative Study of Different Grammar-Based Genetic Programming Approaches

Cited by 19 publications

References 12 publications

Understanding Grammatical Evolution: Grammar Design

Understanding Grammatical Evolution: Grammar Design

Structured grammatical evolution for glucose prediction in diabetic patients

Weighted Hierarchical Grammatical Evolution

Contact Info

Product

Resources

About