Grammatical Evolution Research Articles

A Comparative Study of Dispatching Rule Representations in Evolutionary Algorithms for the Dynamic Unrelated Machines Environment

Dispatching rules are most commonly used to solve scheduling problems under dynamic conditions. Since designing new dispatching rules is a time-consuming process, it can be automated by using various machine learning and evolutionary computation methods. In previous research, genetic programming has been the most commonly used method for automatically designing new dispatching rules. However, there are many other evolutionary methods that use representations other than genetic programming that can be used to create dispatching rules. Some, such as gene expression programming, have already been used successfully, while others, such as Cartesian genetic programming or grammatical evolution, have not yet been used to generate dispatching rules. In this paper, six different methods (genetic programming, gene expression programming, Cartesian genetic programming, grammatical evolution, stack representation, and analytic programming) for generating dispatching rules for the unrelated machines environment are tested and the results for various scheduling criteria are analysed. It is also analysed how different individual sizes in the tested methods affect the performance and average size of the generated dispatching rules. The results show that, with the exception of grammatical evolution and analytic programming, all tested methods perform quite similarly, with results depending on the selected scheduling criterion. The results also show that Cartesian genetic programming is the most resistant to the occurrence of bloat and evolves dispatching rules with the smallest average size.

Fuzzy Pattern Tree Evolution Using Grammatical Evolution

A novel approach to induce Fuzzy Pattern Trees using Grammatical Evolution is presented in this paper. This new method, called Fuzzy Grammatical Evolution, is applied to a set of benchmark classification problems. Experimental results show that Fuzzy Grammatical Evolution attains similar and oftentimes better results when compared with state-of-the-art Fuzzy Pattern Tree composing methods, namely Fuzzy Pattern Trees evolved using Cartesian Genetic Programming, on a set of benchmark problems. We show that, although Cartesian Genetic Programming produces smaller trees, Fuzzy Grammatical Evolution produces better performing trees. Fuzzy Grammatical Evolution also benefits from a reduction in the number of necessary user-selectable parameters, while Cartesian Genetic Programming requires the selection of three crucial graph parameters before each experiment. To address the issue of bloat, an additional version of Fuzzy Grammatical Evolution using parsimony pressure was tested. The experimental results show that Fuzzy Grammatical Evolution with this extension routinely finds smaller trees than those using Cartesian Genetic Programming without any compromise in performance. To improve the performance of Fuzzy Grammatical Evolution, various ensemble methods were investigated. Boosting was seen to find the best individuals on half the benchmarks investigated.

Blood glucose prediction using multi-objective grammatical evolution: analysis of the “agnostic” and “what-if” scenarios

In this paper we investigate the benefits of applying a multi-objective approach for solving a symbolic regression problem by means of Grammatical Evolution. In particular, we extend previous work, obtaining mathematical expressions to model glucose levels in the blood of diabetic patients. Here we use a multi-objective Grammatical Evolution approach based on the NSGA-II algorithm, considering the root-mean-square error and an ad-hoc fitness function as objectives. This ad-hoc function is based on the Clarke Error Grid analysis, which is useful for showing the potential danger of mispredictions in diabetic patients. In this work, we use two datasets to analyse two different scenarios: What-if and Agnostic, the most common in daily clinical practice. In the What-if scenario, where future events are evaluated, results show that the multi-objective approach improves previous results in terms of Clarke Error Grid analysis by reducing the number of dangerous mispredictions. In the Agnostic situation, with no available information about future events, results suggest that we can obtain good predictions with only information from the previous hour for both Grammatical Evolution and Multi-Objective Grammatical Evolution.

Synthesizing Electrically Equivalent Circuits for Use in Electrochemical Impedance Spectroscopy through Grammatical Evolution

Electrochemical impedance spectroscopy (EIS) is an important electrochemical technique that is used to detect changes and ongoing processes in a given material. The main challenge of EIS is interpreting the collected measurements, which can be performed in several ways. This article focuses on the electrical equivalent circuit (EEC) approach and uses grammatical evolution to automatically construct an EEC that produces an AC response that corresponds to one obtained by the measured electrochemical process(es). For fitting purposes, synthetic measurements and data from measurements in a realistic environment were used. In order to be able to faithfully fit realistic data from measurements, a new circuit element (ZARC) had to be implemented and integrated into the SPICE simulator, which was used for evaluating EECs. Not only is the presented approach able to automatically (i.e., with almost no user input) produce a more than satisfactory EEC for each of the datasets, but it also can also generate completely new EEC configurations. These new configurations may help researchers to find some new, previously overlooked ongoing electrochemical processes.

Exploiting abstractions for grammar‐based learning of complex multi‐agent behaviours

This paper presents a grammar-based evolutionary approach that incorporates abstractions to learn complex collective behaviours through their simpler representations. We propose modifications to the grammar syntax design and genome structure to facilitate evolution of abstractions in separate genome partitions. Two abstraction techniques based on behavioural decomposition and environmental scaffolding are presented to derive these simpler representations. Parallel and incremental learning architectures incorporated with grammatical evolution (GE) are investigated with three complex problems to evaluate their potential in generating collective multi-agent behaviours. The results infer that both learning architectures surpass a generic GE model in performance for evolving complex behaviours. Furthermore, using environmental scaffolding reduces the robustness of the model than when only the behavioural decomposition technique is used. However, it has more potential to generate solutions with better fitness than when scaffoldings are not used. The evaluations suggest that, by incorporating abstraction learning architectures with grammar-based evolution can significantly improve the performance of an agent system in complex problem domains.

Evolving Personalized Content for Super Mario Bros Using Grammatical Evolution

Adapting game content to a particular player's needs and expertise constitutes an important aspect in game design. Most research in this direction has focused on adapting game difficultyto keep the player engaged in the game. Dynamic difficulty adjustment, however, focuses on one aspect of the gameplay experience by adjusting the content to increase ordecrease perceived challenge. In this paper, we introduce a method for automatic level generation for the platform game Super Mario Bros using grammatical evolution. The grammatical evolution-based level generator is used to generate player-adapted content by employing an adaptation mechanism as a fitness function in grammatical evolution to optimizethe player experience of three emotional states: engagement, frustration and challenge. The fitness functions used are models of player experience constructed in our previous work from crowd-sourced gameplay data collected from over 1500 game sessions.

Evolving Playable Content for Cut the Rope through a Simulation-Based Approach

In order to automatically generate high-quality game levels, one needs to be able to automatically verify that the levels are playable. The simulation-based approach to playability testing uses an artificial agent to play through the level, but building such an agent is not always an easy task and such an agent is not always readily available. We discuss this prob- lem in the context of the physics-based puzzle game Cut the Rope, which features continuous time and state space, mak- ing several approaches such as exhaustive search and reactive agents inefficient. We show that a deliberative Prolog-based agent can be used to suggest all sensible moves at each state, which allows us to restrict the search space so that depth-first search for solutions become viable. This agent is successfully used to test playability in Ropossum, a level generator based on grammatical evolution. The method proposed in this paper is likely to be useful for a large variety of games with similar characteristics.

Seeding Grammars in Grammatical Evolution to Improve Search-Based Software Testing

Heuristic-based optimization techniques have been increasingly used to automate different types of code coverage analysis. Several studies suggest that interdependencies (in the form of comparisons) may exist between the condition constructs, of variables and constant values, in the branching conditions of real-world programs, e.g. (i le 100) or (i==j), etc. In this work, by interdependencies we refer to the situations where, to satisfy a branching condition, there must be a certain relationship between the values of some specific condition constructs (which may or may not be a part of the respective condition predicates). For example, the values of variables i and j must be equal to satisfy the condition of (i==j), and the value of variable k must be equal to 100 for the satisfaction of the condition of (k==100). To date, only the Ariadne, a Grammatical Evolution (GE)-based system, exploits these interdependencies between input variables (e.g. of the form (i le j) or (i==j), etc.) to efficiently generate test data. Ariadne employs a simple attribute grammar to exploit these dependencies, which enables it to evolve complex test data, and has been compared favourably to other well-known techniques in the literature. However, Ariadne does not benefit from interdependencies involving constants, e.g. (i le 100) or (j==500), etc., due to the difficulty in evolving precise values, and these are equally important constructs of condition predicates. Furthermore, constant creation in GE can be difficult, particularly with high precision. We propose to seed the grammar with constants extracted from the source code of the program under test to enhance and extend Ariadne’s capability to exploit richer types of dependencies (involving all combinations of both variables and constant values). We compared our results with the original system of Ariadne against a large set of benchmark problems which include 10 numeric programs in addition to the ones originally used for Ariadne. Our results demonstrate that the seeding strategy not only dramatically improves the generality of the system, as it improves the code coverage (effectiveness) by impressive margins, but it also reduces the search budgets (efficiency) often up to an order of magnitude. Moreover, we also performed a rigorous analysis to investigate the scalability of our improved Ariadne, showing that it stays highly scalable when compared to both the original system of Ariadne and GA-based test data generation approach.

Analysis and fast feature selection technique for real-time face detection materials using modified region optimized convolutional neural network

For face-related applications like face recognition and face verification, face detection is vital. Law enforcement agencies extensively use face recognition applications for the detection of known offenders. Security cameras are increasingly deployed everywhere, and these cameras’ captured data can be used for tagging offenders. This is done by offering the face detection system with input images, which are then processed. Later on, attempts will be done to tag the security cameras’ captured data with this processed input data. Effective utilization of deep learning techniques in order to efficiently carry out this task. Computer vision’s various tasks have been taken over by deep Convolutional Neural Networks (CNNs). Of late, the key paradigms for face detection are the region-based CNN (R-CNN) detection models due to their quicker processing speed and progressively better performance. Nevertheless, the huge feature set causes these models’ to be inefficient for real-time face detection. A Grammatical Evolution (GE) Region-Based Fully Conventional Neural Network (R-FCN) for face detection has been proposed in this work in which the GE heuristics are utilized for tuning the hyper-parameters. The WIDER face dataset and Face Detection Dataset and Benchmark (FDDB) dataset were employed for conducting the assessments. In comparison to other advanced paradigms, the proposed technique’s superiority is evident from the experiments conducted.

Grammatical evolution for constraint synthesis for mixed-integer linear programming

Abstract The Mixed-Integer Linear Programming models are a common representation of real-world objects. They support simulation within the expressed bounds using constraints and optimization of an objective function. Unfortunately, handcrafting a model that aligns well with reality is time-consuming and error-prone. In this work, we propose a Grammatical Evolution for Constraint Synthesis (GECS) algorithm that helps human experts by synthesizing constraints for Mixed-Integer Linear Programming models. Given relatively easy-to-provide data of available variables and parameters, and examples of feasible solutions, GECS produces a well-formed Mixed-Integer Linear Programming model in the ZIMPL modeling language. GECS outperforms several previous algorithms, copes well with tens of variables, and seems to be resistant to the curse of dimensionality.

Diachronic Analysis and Deictic Means of French Benevolences and Curses

Benevolences and curses, integral and authentic part of the discourse, most clearly reflect the culture of people. The use of these expressions in the communication space of different languages indicates the need for their study from the standpoint of linguistics, folklore, stylistics, rhetoric, psychology, cultural studies and other sciences. This article provides diachronic analysis and examines the deictic means of French benevolences and curses in the French language. The material for the study was the texts of the Bible, French epic poems, medieval legends, ballads, tales, and fiction. When considering this topic, the author used both general and special scientific methods and techniques: a historical-etymological method, a descriptive method that includes methods for observing, comparing, interpreting and classifying the material being studied; semantic identification method; distribution method. Comparing the medieval French discourse with the modern French language, the author examined the grammatical and semantic evolution, which underwent benevolences and curses.

Feature extraction by grammatical evolution for one-class time series classification

When dealing with a new time series classification problem, modellers do not know in advance which features could enable the best classification performance. We propose an evolutionary algorithm based on grammatical evolution to attain a data-driven feature-based representation of time series with minimal human intervention. The proposed algorithm can select both the features to extract and the sub-sequences from which to extract them. These choices not only impact classification performance but also allow understanding of the problem at hand. The algorithm is tested on 30 problems outperforming several benchmarks. Finally, in a case study related to subject authentication, we show how features learned for a given subject are able to generalise to subjects unseen during the extraction phase.

An Approach for the Generation of Multi-Objective Algorithms Applied to the Integration and Test Order Problem

Multi-Objective Evolutionary Algorithms (MOEAs) have been successfully applied to solve hard real software engineering problems. However, to choose and design a MOEA is considered a difficult task, since there are several parameters and components to be configured. These aspects directly impact the generated solutions and the performance of MOEAs. In this sense, this paper proposes an approach for the automatic generation of MOEAs applied to the Integration and Test Order (ITO) problem. Such a problem refers to the generation of optimal sequences of units for integration testing. The approach includes a set of parameters and components of different MOEAs, and is implemented with two design algorithms: Grammatical Evolution (GE) and Iterated Racing (irace). Evaluation results are presented, comparing the MOEAs generated by both design algorithms. Furthermore, the generated MOEAs are compared to two well-known MOEAs used in the literature to solve the ITO problem. Results show that the MOEAs generated with GE and irace perform similarly, and both outperform traditional MOEAs. The approach can reduce efforts spent to design and configure MOEAs, and serves as basis for implementing solutions to other software engineering problems.

Combining Grammatical Evolution with Modal Interval Analysis: An Application to Solve Problems with Uncertainty

Complex systems are usually affected by various sources of uncertainty, and it is essential to account for mechanisms that ensure the proper management of such disturbances. This paper introduces a novel approach to solve symbolic regression problems, which combines the potential of Grammatical Evolution to obtain solutions by describing the search space with context-free grammars, and the ability of Modal Interval Analysis (MIA) to handle quantified uncertainty. The presented methodology uses an MIA solver to evaluate the fitness function, which represents a novel method to manage uncertainty by means of interval-based prediction models. This paper first introduces the theory that establishes the basis of the proposed methodology, and follows with a description of the system architecture and implementation details. Then, we present an illustrative application example which consists of determining the outer and inner approximations of the mean velocity of the water current of a river stretch. Finally, the interpretation of the obtained results and the limitations of the proposed methodology are discussed.

Roundabout entry capacity models: genetic programming approach

The aim of this study was to develop three roundabout entry capacity models (RECMs) by employing evolutionary-based regression techniques such as genetic programming (GP), age-layered population structure genetic programming (ALPSGP) and grammatical evolution genetic programming (GEGP) in mixed traffic conditions. Necessary data were collected from 27 roundabouts located in eight states in India. The influence area for gap acceptance method was used to determine the critical gap. To assess the significance of the models and select the best, the modified rank index was applied. The results showed that the GEGP model performed better than the GP and ALPSGP models. The GEGP model is also applicable in practice because of its simplicity. Sensitivity analysis revealed that the critical gap is the prime variable in the development of RECMs. The findings of this study should be useful for traffic planners and designers in the capacity estimation of roundabouts in mixed traffic conditions in developing countries with traffic characteristics similar to those in India.

Parametrized Model Predictive Control Approaches for Urban Traffic Networks

Model Predictive Control (MPC) has shown promising results in the control of urban traffic networks, but unfortunately it has one major drawback. The, often nonlinear, optimization that has to be performed at every control time step is computationally too complex to use MPC controllers for real-time implementations (i.e. when the online optimization is performed within the control time interval of the controlled network). This paper proposes an effective parametrized MPC approach to lower the computational complexity of the MPC controller. Two parametrized control laws are proposed that can be used in the parametrized MPC framework, one based on the prediction model of the MPC controllers, and another is constructed using Grammatical Evolution (GE). The performance and computational complexity of the parametrized MPC approach is compared to a conventional MPC controller by performing an extensive simulation-based case study. The simulation results show that for the given case study the parametrized MPC approach is real-time implementable while the performance decreases with less than 3% with respect to the conventional MPC controller.

Grammaticalization of qәl ‘gourd’ in Amharic

The Amharic word qəl ‘gourd’ represents a rare case where a plant term serves as the source of a grammaticalization chain. The development occurred in two stages, first metaphoric change, then grammaticalization proper: gourd > skull/head > Intensive (never Plain) Reflexive (‘he himself, etc.’). This process was entangled with the grammatical evolution of two other words, ras and gəll. Ras, which is the basic unmarked term for ‘head’, as such underwent the basic unmarked grammaticalization into a Plain Reflexive (and only secondarily into an Intensive Reflexive). The other word, gəll ‘separate, individual’, phonetically quite similar to qəl but with no etymological connection to ‘head’, grammaticalized directly to the meaning ‘one’s own, by oneself’, thence secondarily to an Intensive Reflexive (but never a Plain Reflexive). Thus two near-synonyms (qəl, ras ‘head’) underwent two parallel grammaticalizations, but yielding different results: qəl, unlike ras, was never a Plain Reflexive. Why? The distinctive semantic evolution of qəl, I suggest, was partly driven by its phonetic similarity to the historically unrelated gəll, which also was never a Plain Reflexive. The phonetic similarity helped to foster a semantic attraction between the two grammaticalizing morphemes.

Multi-objective Grammatical Evolution of Decision Trees for Mobile Marketing user conversion prediction

The worldwide adoption of mobile devices is raising the value of Mobile Performance Marketing, which is supported by Demand-Side Platforms (DSP) that match mobile users to advertisements. In these markets, monetary compensation only occurs when there is a user conversion. Thus, a key DSP issue is the design of a data-driven model to predict user conversion. To handle this nontrivial task, we propose a novel Multi-objective Optimization (MO) approach to evolve Decision Trees (DT) using a Grammatical Evolution (GE), under two main variants: a pure GE method (MGEDT) and a GE with Lamarckian Evolution (MGEDTL). Both variants evolve variable-length DTs and perform a simultaneous optimization of the predictive performance and model complexity. To handle big data, the GE methods include a training sampling and parallelism evaluation mechanism. The algorithms were applied to a recent database with around 6 million records from a real-world DSP. Using a realistic Rolling Window (RW) validation, the two GE variants were compared with a standard DT algorithm (CART), a Random Forest and a state-of-the-art Deep Learning (DL) model. Competitive results were obtained by the GE methods, which present affordable training times and very fast predictive response times.

Development of a Metaheuristic Programming Method for Synthesis of Nonlinear Models

The solution of the problem of building nonlinear models (mathematical expressions, functions, algorithms, and programs) based on an experimental data set, a set of variables, or a set of basic functions and operations is considered. A metaheuristic programming method for the evolutionary synthesis of nonlinear models has been developed; in this method, a chromosome is represented in the form of a vector of real numbers and allows the use of various bio-inspired (nature-inspired) optimization algorithms in the search for models. The efficiency of the proposed algorithm is estimated using ten bio-inspired algorithms (two modifications of the genetic programming algorithm, differential evolution algorithm, particle swarm optimization algorithm, artificial bee colony algorithm, teaching-learning-based optimization algorithm and two of its modifications, covariance matrix adaptation evolution strategy, and simultaneous heat transfer search algorithm) and compared with the results of standard algorithms of genetic programming, grammatical evolution, and Cartesian genetic programming. The experiments have shown a significant advantage of this approach as compared to the above-mentioned algorithms in terms of both the time needed for finding the solution (greater than by an order of magnitude in most cases) and the probability of finding a given function (model) (greater than twice in many cases).

An improved grammatical evolution approach for generating perturbative heuristics to solve combinatorial optimization problems

Search methodologies such as hyper-heuristics have been successfully used to automate the generation of perturbative heuristics to solve combinatorial optimization problems. However, the domain of automated generation of perturbative heuristics has generally not been well researched and very few works have actually been conducted in the area. In addition, most of the proposed hyper-heuristic methods in the literature simply recombine already existing and human-derived low-level perturbative heuristics (or primitive heuristic components) with various move acceptance criteria to generate the new perturbative heuristics instead of producing the heuristics from scratch. As a result, these methods cannot be applied to problem domains where the human-derived low-level heuristics are not available. The study presented in this paper addresses this issue by proposing an improved approach, based on our previous work, that generates good quality reusable perturbative heuristics from scratch. While this new approach also uses grammatical evolution to generate the heuristics from a set of basic actions and components of the solution (as in our previous work), the grammar has been substantially extended and includes new methods for selecting solution components, conditional constructs, utilizes some information from the solution space as well as extends the syntax of the basic actions in order to cover a wider range of heuristics. Furthermore, the new approach has been applied to a new problem domain, i.e. the boolean satisfiability problem, in addition to the examination timetabling and vehicle routing problems investigated in the earlier work. The experimental results show that the new approach not only generates better perturbative heuristics than those produced in our earlier work, it also produces results that are competitive with those obtained by currently existing generation perturbative hyper-heuristics that have been applied to the benchmark sets in the three domains.