Grammatical Evolution Research Articles

A FPGA-Based Neuromorphic Locomotion System for Multi-Legged Robots

The paper develops a neuromorphic system on a Spartan 6 field programmable gate array (FPGA) board to generate locomotion patterns (gaits) for three different legged robots (biped, quadruped, and hexapod). The neuromorphic system consists of a reconfigurable FPGA-based architecture for a 3G artificial neural network (spiking neural network), which acts as a Central Pattern Generator (CPG). The locomotion patterns, are then generated by the CPG through a general neural architecture, which parameters are offline estimated by means of grammatical evolution and Victor-Purpura distance-based fitness function. The neuromorphic system is fully validated on real biped, quadruped, and hexapod robots.

Low-latency liquidity inefficiency strategies

The vast amount of high-frequency data heralds the use of new methods in financial data analysis and quantitative trading. This study delivers a proof-of-concept for a high frequency-based trading system based on an evolutionary computation method. Motivated by a theoretical liquidity asymmetry theorem from the market microstructure literature, grammatical evolution is used to exploit volume inefficiencies at the bid–ask spread. Using NASDAQ Historical TotalView-ITCH level two limit order book data, execution volumes can be tracked. This allows for testing of the strategies with minimal assumptions. The system evolves profitable and robust strategies with high returns and low risk.

A competence-performance based model to develop a syntactic language for artificial agents

The hypothesis of language use is an attractive theory in order to explain how natural languages evolve and develop in social populations. In this paper we present a model partially based on the idea of language games, so that a group of artificial agents are able to produce and share a symbolic language with syntactic structure. Grammatical structure is induced by grammatical evolution of stochastic regular grammars with learning capabilities, while language development is refined by means of language games where the agents apply on-line probabilistic reinforcement learning. Within this framework, the model adapts the concepts of competence and performance in language, as they have been proposed in some linguistic theories. The first experiments in this article have been organized around the linguistic description of visual scenes with the possibility of changing the referential situations. A second and more complicated experimental setting is also analyzed, where linguistic descriptions are enforced to keep word order constraints.

Automated Conjecturing of Frobenius Numbers via Grammatical Evolution

Conjecturing formulas and other symbolic relations occurs frequently in number theory and combinatorics. If we could automate conjecturing, we could benefit not only from faster conjecturing but also from finding conjectures previously out of our grasp. Grammatical evolution (GE), a genetic programming technique, can be used for automated conjecturing in mathematics. Concretely, this work describes how one can interpret the Frobenius problem as a symbolic regression problem, and then apply GE to it. In this manner, a few formulas for Frobenius numbers of specific quadruples were found automatically. The sketch of the proof of one conjectured formula, using lattice point enumeration method, is provided as well. The same method can easily be used on other problems to speed up and enhance the research process.

Runtime data center temperature prediction using Grammatical Evolution techniques

Data Centers are huge power consumers, both because of the energy required for computation and the cooling needed to keep servers below thermal redlining. The most common technique to minimize cooling costs is increasing data room temperature. However, to avoid reliability issues, and to enhance energy efficiency, there is a need to predict the temperature attained by servers under variable cooling setups. Due to the complex thermal dynamics of data rooms, accurate runtime data center temperature prediction has remained as an important challenge. By using Grammatical Evolution techniques, this paper presents a methodology for the generation of temperature models for data centers and the runtime prediction of CPU and inlet temperature under variable cooling setups. As opposed to time costly Computational Fluid Dynamics techniques, our models do not need specific knowledge about the problem, can be used in arbitrary data centers, re-trained if conditions change and have negligible overhead during runtime prediction. Our models have been trained and tested by using traces from real Data Center scenarios. Our results show how we can fully predict the temperature of the servers in a data rooms, with prediction errors below 2°C and 0.5°C in CPU and server inlet temperature respectively.

Discrete Planar Truss Optimization by Node Position Variation Using Grammatical Evolution

The majority of existing discrete truss optimization methods focus primarily on optimizing global truss topology using a ground structure approach, in which all possible node and beam locations are specified a priori . The ground structure discrete optimization method has been shown to be restrictive as it limits derivable solutions to what is explicitly defined. Greater representational freedom can improve performance. In this paper, grammatical evolution is applied. It can represent a variable number of nodes and their locations on a continuum. A novel method of connecting evolved nodes using a Delaunay triangulation algorithm shows that fully triangulated, kinematically stable structures can be generated. Discrete beam-truss structures can be optimized without the need for any information about the desired form of the solution other than the design envelope. Our technique is compared to existing discrete optimization techniques, and notable savings in structure self-weight are demonstrated. In particular, our new method can produce results superior to those reported in the literature in cases in which the problem is ill-defined and the structure of the solution is not known a priori .

UMBGE: Univariate Model Based Grammatical Evolution

UMBGE: Univariate Model Based Grammatical Evolution

Identification of genetic interaction networks via an evolutionary algorithm evolved Bayesian network.

BackgroundThe future of medicine is moving towards the phase of precision medicine, with the goal to prevent and treat diseases by taking inter-individual variability into account. A large part of the variability lies in our genetic makeup. With the fast paced improvement of high-throughput methods for genome sequencing, a tremendous amount of genetics data have already been generated. The next hurdle for precision medicine is to have sufficient computational tools for analyzing large sets of data. Genome-Wide Association Studies (GWAS) have been the primary method to assess the relationship between single nucleotide polymorphisms (SNPs) and disease traits. While GWAS is sufficient in finding individual SNPs with strong main effects, it does not capture potential interactions among multiple SNPs. In many traits, a large proportion of variation remain unexplained by using main effects alone, leaving the door open for exploring the role of genetic interactions. However, identifying genetic interactions in large-scale genomics data poses a challenge even for modern computing.ResultsFor this study, we present a new algorithm, Grammatical Evolution Bayesian Network (GEBN) that utilizes Bayesian Networks to identify interactions in the data, and at the same time, uses an evolutionary algorithm to reduce the computational cost associated with network optimization. GEBN excelled in simulation studies where the data contained main effects and interaction effects. We also applied GEBN to a Type 2 diabetes (T2D) dataset obtained from the Marshfield Personalized Medicine Research Project (PMRP). We were able to identify genetic interactions for T2D cases and controls and use information from those interactions to classify T2D samples. We obtained an average testing area under the curve (AUC) of 86.8 %. We also identified several interacting genes such as INADL and LPP that are known to be associated with T2D.ConclusionsDeveloping the computational tools to explore genetic associations beyond main effects remains a critically important challenge in human genetics. Methods, such as GEBN, demonstrate the utility of considering genetic interactions, as they likely explain some of the missing heritability.

문법적 진화기법과 조건부 확률을 이용한 청소 로봇의 이동 패턴 계획

청소 로봇은 가정에서 사용 가능한 대표적인 지능형 로봇이다. 고가형 청소 로봇은 센서로부터 정보를 제공받아 높은 커버리지 성능을 가진 알고리즘이 존재하지만, 저가형의 청소 로봇엔 적용하기 어렵다. 본 논문은 저가형의 청소 로봇과 같은 환경에서 효율적인 움직임을 구현하기 위해 문법적 진화기법기반의 청소 로봇의 이동 패턴을 계획하는 알고리즘을 제안한다. 이를 위해 배커스-나우르 표기법을 사용하여 이동 패턴 문법을 정의하고 진화연산을 통해 최적화된 프로그램을 생성하였다. 이와 더불어 프로그램생성 과정에서 획득한 문법 요소 간 조건부 확률 정보를 활용하였다. 제안 알고리즘의 성능 검증을 위해 청소 로봇 시뮬레이션을 활용하여 기존 알고리즘과 성능을 비교하였으며 실험 결과를 통해 본 논문에서 제안한 기법의 효율성을 확인하였다.

Model approach to grammatical evolution: deep-structured analyzing of model and representation

Grammatical evolution (GE) is a combination of genetic algorithm and context-free grammar, evolving programs for given problems by breeding candidate programs in the context of a grammar using genetic operations. As far as the representation is concerned, classical GE as well as most of its existing variants lacks awareness of both syntax and semantics, therefore having no potential for parallelism of various evaluation methods. To this end, we have proposed a novel approach called model-based grammatical evolution (MGE) in terms of grammar model (a finite state transition system) previously. It is proved, in the present paper, through theoretical analysis and experiments that semantic embedded syntax taking the form of regex (regular expression) over an alphabet of simple cycles and paths provides with potential for parallel evaluation of fitness, thereby making it possible for MGE to have a better performance in coping with more complex problems than most existing GEs.

Optimization of neural networks through grammatical evolution and a genetic algorithm

This paper proposes a hybrid neuro-evolutive algorithm (NEA) that uses a compact indirect encoding scheme (IES) for representing its genotypes (a set of ten production rules of a Lindenmayer System with memory), moreover has the ability to reuse the genotypes and automatically build modular, hierarchical and recurrent neural networks. A genetic algorithm (GA) evolves a Lindenmayer System (L-System) that is used to design the neural network’s architecture. This basic neural codification confers scalability and search space reduction in relation to other methods. Furthermore, the system uses a parallel genome scan engine that increases both the implicit parallelism and convergence of the GA. The fitness function of the NEA rewards economical artificial neural networks (ANNs) that are easily implemented. The NEA was tested on five real-world classification datasets and three well-known datasets for time series forecasting (TSF). The results are statistically compared against established state-of-the-art algorithms and various forecasting methods (ADANN, ARIMA, UCM, and Forecast Pro). In most cases, our NEA outperformed the other methods, delivering the most accurate classification and time series forecasting with the least computational effort. These superior results are attributed to the improved effectiveness and efficiency of NEA in the decision-making process. The result is an optimized neural network architecture for solving classification problems and simulating dynamical systems.

The training set and generalization in grammatical evolution for autonomous agent navigation

Over recent years, evolutionary computation research has begun to emphasize the issue of generalization. Instead of evolving solutions that are optimized for a particular problem instance, the goal is to evolve solutions that can generalize to various different scenarios. This paper compares objective-based search and novelty search on a set of generalization oriented experiments for a navigation task using grammatical evolution (GE). In particular, this paper studies the impact that the training set has on the generalization of evolved solutions, considering: (1) the training set size; (2) the manner in which the training set is chosen (random or manual); and (3) if the training set is fixed throughout the run or dynamically changed every generation. Experimental results suggest that novelty search outperforms objective-based search in terms of evolving navigation behaviors that are able to cope with different initial conditions. The traditional objective-based search requires larger training sets and its performance degrades when the training set is not fixed. On the other hand, novelty search seems to be robust to different training sets, finding general solutions in almost all of the studied conditions with almost perfect generalization in many scenarios.

Complex diseases SNP selection and classification by hybrid Association Rule Mining and Artificial Neural Network—based Evolutionary Algorithms

Recently, various techniques have been applied to classify Single Nucleotide Polymorphisms (SNP) data as they have been shown to be implicated in various human diseases. One of the major problems related to SNP sets is the large p, small n problem which refers to the high number of features and the small number of samples, which makes the classification task complex. In this paper, a new hybrid intelligent technique based on Association Rule Mining (ARM) and Neural Networks (NN) which uses Evolutionary Algorithms (EA) is proposed to deal with the dimensionality problem. On the one hand, ARM optimized by Grammatical Evolution (GE) is used to select the most informative features and to reduce the dimensionality by parallel extraction of associations between SNPs in two separate datasets of case and control samples. On the other hand, and to complement the previous task, a NN is used for efficient classification. The Genetic Algorithm (GA) is used for setting up the parameters of the two combined techniques. The proposed GA-NN-GEARM approach has been applied on four different SNP datasets obtained from the NCBI Gene Expression Omnibus (GEO) website. The created model has reached a high classification accuracy, reaching in some cases 100%, and has outperformed several feature selection techniques when combined with different classifiers.

Unveiling the properties of structured grammatical evolution

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.

Grammatical evolution using two-dimensional gene for symbolic regression: an advanced improvement with conditional statement grammar

Symbolic regression problems can be solved using grammatical evolution (GE), an evolutionary computation (EC) method, to find a function that coincides satisfactorily with the given datasets. The evolutional approach of GE is based on the grammar learning paradigm, which can translate the genotype (binary digit) into the phenotype (terminals and non-terminals). Unlike traditional codons in a genotype, the fittest codons in phenotype represented by the Backus-Naur form (BNF) are difficult for next generation genes to inherit the traits of parents, accounting for crossover and mutation. For this issue, this article presents a proposal of an advanced improvement to GE using a two-dimensional gene (GE2DG). In contrast to multi-chromosomal GE (GEMC), our proposal not only encloses the two-dimensional gene-expression for symbolic regression, but also introduces one independent gene defined as a conditional statement to express a new BNF grammar of an if-then (-else) branch. In the experiments described herein, continuous/discontinuous non-branch functions and continuous/discontinuous branch functions, four testing patterns, are considered as numerical examples. Results show that GE2DG has better performance than the original GE or GEMC. Especially for the case of branch functions, GE with hybrid chromosome (GEHC), where GE2DG is incorporated with GEMC, has faster convergence in symbolic regression than other methods.

Grammatical evolution using two-dimensional gene for symbolic regression: an advanced improvement with conditional statement grammar

Grammatical evolution using two-dimensional gene for symbolic regression: an advanced improvement with conditional statement grammar

GramEvol: Grammatical Evolution inR

We describe an R package which implements grammatical evolution (GE) for automatic program generation. By performing an unconstrained optimization over a population of R expressions generated via a user-defined grammar, programs which achieve a desired goal can be discovered. The package facilitates the coding and execution of GE programs, and supports parallel execution. In addition, three applications of GE in statistics and machine learning, including hyper-parameter optimization, classification and feature generation are studied.

文法差分進化の改良について

文法差分進化の改良について

A Conditional Dependency Based Probabilistic Model Building Grammatical Evolution

A Conditional Dependency Based Probabilistic Model Building Grammatical Evolution

Grammatical evolution in developing optimal inventory policies for serial and distribution supply chains

Recently, there has been a growing literature on biologically inspired algorithms, particularly genetic algorithms and genetic programming, applied to supply chain modelling and inventory control optimisation. Due to the rigidity of the genetic algorithms approach, it is difficult to change the underlying model logic and add richness to the supply chain. While genetic programming provides a more flexible approach than that provided by genetic algorithms, to date its application has been limited to small supply chain modelling problems in relation to optimal inventory policies. This research applies Grammatical Evolution, a relatively new biologically inspired algorithm, to the field of supply chain optimisation, employing human readable rules called grammars. These grammars provide a single mechanism to describe a variety of complex structures and can incorporate the domain knowledge of the practitioner to bias the algorithm towards regions of the search space containing better solutions. Results are presented showing Grammatical Evolution is at least competitive in cost terms, and superior in flexibility, with these methods applicable to any supply chain of the serial or distribution type. Furthermore, Grammatical Evolution shows an adaptive ability that augurs well for supply chains in dynamic environments, such as disruption.