Dejan Hrncic

A parameter control method of evolutionary algorithms using exploration and exploitation measures with a practical application for fitting Sovova's mass transfer model

Exploration and exploitation are omnipresent terms in evolutionary computation community that have been broadly utilized to explain how evolutionary algorithms perform search. However, only recently exploration and exploitation measures were presented in a quantitative way enabling to measure amounts of exploration and exploitation. To move a step further, this paper introduces a parameter control approach that utilizes such measures as feedback to adaptively control evolution processes. The paper shows that with new exploration and exploitation measures, the evolution process generates relatively well results in terms of fitness and/or convergence rate when applying to a practical chemical engineering problem of fitting Sovovas model. We also conducted an objective statistical analysis using Bonferroni-Dunn test and sensitivity analysis on the experimental results. The statistical analysis results again proved that the parameter control strategy using exploration and exploitation measures is competitive to the other approaches presented in the paper. The sensitivity analysis results also showed that different initial values may affect output in different magnitude.

A memetic grammar inference algorithm for language learning

An unsupervised incremental algorithm for grammar inference and its application to domain-specific language development are described. Grammatical inference is the process of learning a grammar from the set of positive and optionally negative sentences. Learning general context-free grammars is still considered a hard problem in machine learning and is not completely solved yet. The main contribution of the paper is a newly developed memetic algorithm, which is a population-based evolutionary algorithm enhanced with local search and a generalization process. The learning process is incremental since a new grammar is obtained from the current grammar and false negative samples, which are not parsed by the current grammar. Despite being incremental, the learning process is not sensitive to the order of samples. All important parts of this algorithm are explained and discussed. Finally, a case study of a domain specific language for rendering graphical objects is used to show the applicability of this approach.

Grammar inference algorithms and applications in software engineering

Many problems exist whose solutions take the form of patterns that may be expressed using grammars (e.g., speech recognition, text processing, genetic sequencing). Construction of these grammars is usually carried out by computer scientists working with domain experts. In the case when there is a lack of domain experts, grammar inference can be applied. In this paper, two grammar inference algorithms are briefly described and their application to software engineering is presented.

Implementation of EasyTime formal semantics using a LISA compiler generator

A manual measuring time tool in mass sporting competitions would not be imaginable nowadays, because many modern disciplines, such as IRONMAN, last a long-time and, therefore, demand additional reliability. Moreover, automatic timing-devices based on RFID technology, have become cheaper. However, these devices cannot operate as stand-alone because they need a computer measuring system that is capable of processing incoming events, encoding the results, assigning them to the correct competitor, sorting the results according to the achieved times, and then providing a printout of the results. This article presents the domain-specific language EasyTime, which enables the controlling of an agent by writing the events within a database. It focuses, in particular, on the implementation of EasyTime with a LISA tool that enables the automatic construction of compilers from language specifications, using Attribute Grammars.

Improving Grammar Inference by a Memetic Algorithm

A memetic algorithm, a novel approach for solving NP-hard problems, has been applied in this paper for grammatical inference in the field of domain-specific languages (DSLs). DSLs are often designed by domain experts who have no knowledge about the syntax and semantics of programming languages. However, they are able to write sample programs to accomplish their goals and illustrate the features of their language. Grammatical inference is a technique to infer a context-free grammar from a set of positive (and negative) samples. This paper shows that grammatical inference may assist domain experts and software language engineers in developing DSLs by automatically producing a grammar, which describes a set of sample DSL programs. A memetic-algorithm-based tool is developed, which greatly improves results and robustness of the inference process.

Fitting Sovova's mass transfer model using an evolutionary algorithm and differential evolution

In chemical engineering, reliable models are necessary to reduce the cost of process design. An evolutionary algorithm with resizable population was used to estimate coefficients of Sovovas mass transfer model and was compared with a global optimiser found in the literature and commonly used differential evolution algorithm. Comparison of the evolutionary algorithm to the global optimisation technique proved that the evolutionary algorithm is more robust, efficient, and significantly better than the global optimiser in regards to the deviation of the model from experimental data. It is also shown that the proposed evolutionary algorithm performed better than differential evolution algorithm.

MARS: Metamodel Recovery from Multi-tiered Models Using Grammar Inference

In model-driven engineering, metamodels may get lost over time resulting in the inability to load and view existing model instances. MARS is a system that recovers metamodels from model instances using grammar inference. This paper discusses advances in MARS that improve accuracy and scalability.

Grammar inference technology applications in software engineering

While Grammar Inference (GI) has been successfully applied to many diverse domains such as speech recognition and robotics, its application to software engineering has been limited, despite wide use of context-free grammars in software systems. This paper reports current developments and future directions in the applicability of GI to software engineering, where GI is seen to offer innovative solutions to the problems of inference of domain-specific language (DSL) specifications from example DSL programs and recovery of metamodels from instance models.