SGE: A Structured Representation for Grammatical Evolution

Evolutionary algorithms (EAs) have proven to be effective in tackling problems in many different domains. However, users are often required to spend a significant amount of effort in fine-tuning the EA parameters in order to make the algorithm work. In principle, visualization tools may be of great help in this laborious task, but current visualization tools are either EA-specific, and hence hardly available to all users, or too general to convey detailed information. In this work, we study the Diversity and Usage map (DU map), a compact visualization for analyzing a key component of every EA, the representation of solutions. In a single heat map, the DU map visualizes for entire runs how diverse the genotype is across the population and to which degree each gene in the genotype contributes to the solution. We demonstrate the generality of the DU map concept by applying it to six EAs that use different representations (bit and integer strings, trees, ensembles of trees, and neural networks). We present the results of an online user study about the usability of the DU map which confirm the suitability of the proposed tool and provide important insights on our design choices. By providing a visualization tool that can be easily tailored by specifying the diversity (D) and usage (U) functions, the DU map aims at being a powerful analysis tool for EAs practitioners, making EAs more transparent and hence lowering the barrier for their use.

Section: Du Map On Gementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Unveiling evolutionary algorithm representation with DU maps

Medvet

Virgolin

Castelli

et al. 2018

“…Moreover, the one-to-one correspondence between genes and non-terminals removes the need to rely on the modulo operation in the genotype-phenotype mapping, thus reducing the redundancy levels. The effectiveness of SGE was first assessed on a preliminary set of problems [20] and results were encouraging, as the new representation was able to obtain good optimization results. Also, it proved to be efficient, since it needed a lower number of evaluations to discover good quality solutions.…”

Section: Introductionmentioning

confidence: 98%

“…Structured grammatical evolution (SGE) is a novel genotypic representation for GE, where each gene is linked to a non-terminal of the grammar being used [20]. SGE ensures that the modification of a gene does not affect the derivation options of other non-terminals, thereby increasing locality.…”

Section: Introductionmentioning

confidence: 99%

Unveiling the properties of structured grammatical evolution

Lourenço

Pereira

Costa

2016

Self Cite

Structured grammatical evolution (SGE) is a new genotypic representation for grammatical evolution (GE). It comprises a hierarchical organization of the genes, where each locus is explicitly linked to a non-terminal of the grammar being used. This one-to-one correspondence ensures that the modification of a gene does not affect the derivation options of other non-terminals. We present a comprehensive set of optimization results obtained with problems from three different categories: symbolic regression, path finding, and predictive modeling. In most of the situations SGE outperforms standard GE, confirming the effectiveness of the new representation. To understand the reasons for SGE enhanced performance, we scrutinize its main features. We rely on a set of static measures to model the interactions between the representation and variation operators and assess how they influence the interplay between the genotype-phenotype spaces. The study reveals that the structured organization of SGE promotes an increased locality and is less redundant than standard GE, thus fostering an effective exploration of the search space.

“…Second, according to its inventors, the overall GE framework was directly inspired by Nature [22]. Third, the properties of the GE genotype-phenotype mapping function have been widely studied [38,37,15]: indeed, those properties eventually served as main goals while designing new GE variants, essentially consisting in new mapping functions which were shown to be more effective than the original approach [12,16].…”

mentioning

confidence: 99%

Designing automatically a representation for grammatical evolution

Medvet

Bartoli

Lorenzo

et al. 2018

A long-standing problem in Evolutionary Computation consists in how to choose an appropriate representation for the solutions. In this work we investigate the feasibility of synthesizing a representation automatically, for the large class of problems whose solution spaces can be defined by a context-free grammar. We propose a framework based on a form of meta-evolution in which individuals are candidate representations expressed with an ad hoc language that we have developed to this purpose. Individuals compete and evolve according to an evolutionary search aimed at optimizing such representation properties as redundancy, uniformity of redundancy, and locality. We assessed experimentally three variants of our framework on established benchmark problems and compared the resulting representations to human-designed representations commonly used (e.g., classical Grammatical Evolution). The results are promising as the evolved representations indeed exhibit better properties than the human-designed ones. Furthermore, the evolved representations compare favorably with the human-designed baselines in search effectiveness as well. Specifically, we select a best evolved representation as the representation with best search effectiveness on a set of learning problems and assess its effectiveness on a separate set of challenging validation problems. For each of the three proposed variants of our framework, the best evolved representation exhibits an average fitness rank on the set of validation problems that is better than the average fitness rank of the human-designed baselines on the same problems.