Structured Grammatical Evolution: A Dynamic Approach

Proceedings of the Genetic and Evolutionary Computation Conference

Colmenar

Hidalgo

et al. 2019

Self 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;

Section: Genetic Programming and Grammarsmentioning

confidence: 99%

“…Each gene has the list of integers needed for expanding <start>, <expr>, <op> and <value>, respectively. Adapted from [11].…”

Section: Genetic Programming and Grammarsmentioning

confidence: 99%

Structured grammatical evolution for glucose prediction in diabetic patients

Proceedings of the Genetic and Evolutionary Computation Conference

Colmenar

Hidalgo

et al. 2019

Self Cite

“…To properly introduce DSGE [2] we must start by detailing Structured Grammatical Evolution (SGE) [21], which is a variant of Grammatical Evolution (GE) [22]. The three methods are grammar-based GP approaches, and thus the search space is defined by means of a Context-Free Grammar (CFG).…”

Section: Dynamic Structured Grammatical Evolutionmentioning

confidence: 99%

Evolution of Scikit-Learn Pipelines with Dynamic Structured Grammatical Evolution

Assunção

Lecture Notes in Computer Science

Ribeiro

et al. 2020

Self Cite

The deployment of Machine Learning (ML) models is a difficult and time-consuming job that comprises a series of sequential and correlated tasks that go from the data pre-processing, and the design and extraction of features, to the choice of the ML algorithm and its parameterisation. The task is even more challenging considering that the design of features is in many cases problem specific, and thus requires domain-expertise. To overcome these limitations Automated Machine Learning (AutoML) methods seek to automate, with few or no human-intervention, the design of pipelines, i.e., automate the selection of the sequence of methods that have to be applied to the raw data. These methods have the potential to enable non-expert users to use ML, and provide expert users with solutions that they would unlikely consider. In particular, this paper describes AutoML-DSGE -a novel grammar-based framework that adapts Dynamic Structured Grammatical Evolution (DSGE) to the evolution of Scikit-Learn classification pipelines. The experimental results include comparing AutoML-DSGE to another grammar-based AutoML framework, Resilient Classification Pipeline Evolution (RECIPE), and show that the average performance of the classification pipelines generated by AutoML-DSGE is always superior to the average performance of RECIPE; the differences are statistically significant in 3 out of the 10 used datasets.

“…The genotype of the candidate solutions is organised into two levels: (i) the outer-level encodes the sequence of evolutionary units (with respect to the macrostructure), and sets the non-terminal symbol that is used as initial symbol for the grammatical derivation; and (ii) the inner-level corresponds to each outer-level position and encodes the parameters of a specific evolutionary unit. The innerlevel genotype is similar to the genotype of Dynamic Structured Grammatical Evolution (DSGE); for more details on DSGE refer to [23]. An example of the genotype and corresponding phenotype of a candidate solution are represented in Figures 2 and 3, respectively.…”

Section: Fast-densermentioning

confidence: 99%

Incremental Evolution and Development of Deep Artificial Neural Networks

Assunção

Lecture Notes in Computer Science

Ribeiro

et al. 2020

Self Cite

NeuroEvolution (NE) methods are known for applying Evolutionary Computation to the optimisation of Artificial Neural Networks (ANNs). Despite aiding non-expert users to design and train ANNs, the vast majority of NE approaches disregard the knowledge that is gathered when solving other tasks, i.e., evolution starts from scratch for each problem, ultimately delaying the evolutionary process. To overcome this drawback, we extend Fast Deep Evolutionary Network Structured Representation (Fast-DENSER) to incremental development. We hypothesise that by transferring the knowledge gained from previous tasks we can attain superior results and speedup evolution. The results show that the average performance of the models generated by incremental development is statistically superior to the non-incremental average performance. In case the number of evaluations performed by incremental development is smaller than the performed by non-incremental development the attained results are similar in performance, which indicates that incremental development speeds up evolution. Lastly, the models generated using incremental development generalise better, and thus, without further evolution, report a superior performance on unseen problems.