Is a comparison of results meaningful from the inexact replications of computational experiments?

The main objective of this paper is to correct the unreasonable and inaccurate criticism to our previous experiments using Teaching–Learning-Based Optimization algorithm and to quantify the amount of error that may arise due to incorrect counting of fitness evaluations. It is shown that inexact experiment replication should be avoided in comparisons between meta-heuristic algorithms whenever possible. Otherwise, an inexact replication and margin of error should be explicitly reported

Graph Grammar Induction as a Parser-Controlled Heuristic Search Process

A graph grammar is a generative description of a graph language (a possibly infinite set of graphs). In this paper, we present a novel algorithm for inducing a graph grammar from a given set of 'positive' and 'negative' graphs. The algorithm is guaranteed to produce a grammar that can generate all of the positive and none of the negative input graphs. Driven by a heuristic specific-to-general search process, the algorithm tries to find a small grammar that generalizes beyond the positive input set. During the search, the algorithm employs a graph grammar parser to eliminate the candidate grammars that can generate at least one negative input graph. We validate our method by inducing grammars for chemical structural formulas and flowcharts and thereby show its potential applicability to chemical engineering and visual programming

Improving the graph grammar parser of Rekers and Schürr

Graph grammars and graph grammar parsers are to visual languages what string grammars and parsers are to textual languages. A graph grammar specifies a set of valid graphs and can thus be used to formalise the syntax of a visual language. A graph grammar parser is a tool for recognising valid programs in such a formally defined visual language. A parser for context-sensitive graph grammars, which have proved to be suitable for formalising real-world visual languages, was developed by Rekers and Schürr. We propose three improvements of this parser. One of them enlarges the class of parsable graph grammars, while the other two increase the parser’s computational efficiency. Experimental results show that for some (meaningful) graph grammars, our improvements can enhance the parser’s performance by orders of magnitude. The proposed improvements will hopefully increase both the parser’s applicability and the interest in visual language parsing in general

Development of a tool for microservices orchestration in online stores

V diplomskem delu smo opisali razvoj produkta TRONintegration. Produkt služi kot orodje za izdelavo integracije dveh sistemov (na primer TRONoffice in spletne trgovine Shopify). Orodje v osnovi temelji na podobnih funkcionalnostih obstoječega produkta Tray.io, vendar z veliko več možnostmi in dodelavami ter samostojno implementacijo. Opisali smo uporabljene tehnologije, raziskali smo knjižnico Syncfusion, ki nam je zelo pripomogla pri vizualnem prikazu diagrama. Ta najbolj pomembni del predstavlja potek same integracije in je sestavljen iz gradnikov: vozlišč in povezav. Raziskali smo področje orkestracije mikrostoritev in nato razvijali produkt po fazah ter ga na koncu testirali. S pomočjo dokumentacije smo uspešno izdelali orodje, ki omogoča izdelavo integracije dveh sistemov na intuitiven vizualen način in zanj ni potrebno veliko predznanja.In the thesis, we described the development of the TRONintegration product. The product serves as a tool for creating integration between two systems (for example, TRONoffice and the Shopify web store). The tool is primarily based on similar functionalities as the existing Tray.io product but with many more options, enhancements, and standalone implementation. We described the technologies used and explored the Syncfusion library, which greatly helped us with the visual representation of the diagram. This most important part presents the flow of the integration itself and is composed of components: nodes and connections. We explored the field of microservices orchestration and then developed the product in phases, testing it in the end. With the help of documentation, we successfully created a tool that allows the creation of integration between two systems in an intuitive visual manner, requiring minimal prior knowledge

A note on teaching–learning-based optimization algorithm

Teaching–Learning-Based Optimization (TLBO) seems to be a rising star from amongst a number of metaheuristics with relatively competitive performances. It is reported that it outperforms some of the well-known metaheuristics regarding constrained benchmark functions, constrained mechanical design, and continuous non-linear numerical optimization problems. Such a breakthrough has steered us towards investigating the secrets of TLBO’s dominance. This paper reports our findings on TLBO qualitatively and quantitatively through code-reviews and experiments, respectively. Our findings have revealed three important mistakes regarding TLBO: (1) at least one unreported but important step; (2) incorrect formulae on a number of fitness function evaluations; and (3) misconceptions about parameter-less control. Additionally, unfair experimental settings/conditions were used to conduct experimental comparisons (e.g., different stopping criteria). The experimental results for constrained and unconstrained benchmark functions under fairly equal conditions failed to validate its performance supremacy. The ultimate goal of this paper is to provide reminders for metaheuristics’ researchers and practitioners in order to avoid similar mistakes regarding both the qualitative and quantitative aspects, and to allow fair comparisons of the TLBO algorithm to be made with other metaheuristic algorithms