Predicting IR personalization performance using pre-retrieval query predictors

Code reviews consist in proof-reading proposed code changes in order to find their shortcomings such as bugs, insufficient test coverage or misused design patterns. Code reviews are conducted before merging submitted changes into the main development branch. The selection of suitable reviewers is crucial to obtain the high quality of reviews. In this article we present a new method of recommending reviewers for code changes. This method is based on profiles of individual programmers. For each developer we maintain his/her profile. It is the multiset of all file path segments from commits reviewed by him/her. It will get updated when he/she presents a new review. We employ a similarity function between such profiles and change proposals to be reviewed. The programmer whose profile matches the change most is recommended to become the reviewer. We performed an experimental comparison of our method against state-of-the-art techniques using four large open-source projects. We obtained improved results in terms of classification metrics (precision, recall and F-measure) and performance (we have lower time and space complexity).     

Automatic mineral identification using genetic programming

Automatic mineral identification using evolutionary computation technology is discussed. Thin sections of mineral samples are photographed digitally using a computer-controlled rotating polarizer stage on a petrographic microscope. A suite of image processing functions is applied to the images. Filtered image data for identified mineral grains is then selected for use as training data for a genetic programming system, which automatically synthesizes computer programs that identify these grains. The evolved programs use a decision-tree structure that compares the mineral image values with one other, resulting in a thresholding analysis of the multi-dimensional colour and textural space of the mineral images. Received: 18 October 1999 / Accepted: 20 January 2001     

EEG based personality prediction using genetic programming

The reliable correlation between personality and brain signal ensures that inferences from cognitive processes can be achieved. This research aims primarily to predict one's personality using brain signals. On grounds of Psychology, the inference of personality in this work is performed on the basis of the Myers–Briggs Type Indicator (MBTI) personality inventory. Personality consists of different types of thinking, feeling and behavior patterns. EEG signals are produced when a person is exposed to situations or scenarios via visual information and experiences various emotions or sentiments. In this study, by evaluating brain waves while a person watches personality traits elicitation materials, the identification of the personality traits of an individual is done. The elicitation materials used for the collection of the dataset comprise approximately 50 videos with the pre-defined personality of the dramatic personae and therefore, it is considered to be the ground truth for the experimental procedure of this work. For creating a dataset, sixty participants contributed and gave brain signals. The GP model with the proposed BSH crossover, known as the BSHGP model, is implemented. The maximum performance of the BSHGP model for a 10-fold partition scheme is 84.34%.     

Evolving evolutionary algorithms using linear genetic programming

A new model for evolving Evolutionary Algorithms is proposed in this paper. The model is based on the Linear Genetic Programming (LGP) technique. Every LGP chromosome encodes an EA which is used for solving a particular problem. Several Evolutionary Algorithms for function optimization, the Traveling Salesman Problem and the Quadratic Assignment Problem are evolved by using the considered model. Numerical experiments show that the evolved Evolutionary Algorithms perform similarly and sometimes even better than standard approaches for several well-known benchmarking problems.     

Functional search in economics using genetic programming

This paper discusses economic applications of a recently developed artificial intelligence technique-Koza's genetic programming (GP). GP is an evolutionary search method related to genetic algorithms. In GP, populations of potential solutions consist of executable computer algorithms, rather than coded strings. The paper provides an overview of how GP works, and illustrates with two applications: solving for the policy function in a simple optimal growth model, and estimating an unusual regression function. Results suggest that the GP search method can be an interesting and effective tool for economists.     

Parallel evolution using multi-chromosome cartesian genetic programming

Parallel and distributed methods for evolutionary algorithms have concentrated on maintaining multiple populations of genotypes, where each genotype in a population encodes a potential solution to the problem. In this paper, we investigate the parallelisation of the genotype itself into a collection of independent chromosomes which can be evaluated in parallel. We call this multi-chromosomal evolution (MCE). We test this approach using Cartesian Genetic Programming and apply MCE to a series of digital circuit design problems to compare the efficacy of MCE with a conventional single chromosome approach (SCE). MCE can be readily used for many digital circuits because they have multiple outputs. In MCE, an independent chromosome is assigned to each output. When we compare MCE with SCE we find that MCE allows us to evolve solutions much faster. In addition, in some cases we were able to evolve solutions with MCE that we unable to with SCE. In a case-study, we investigate how MCE can be applied to to a single objective problem in the domain of image classification, namely, the classification of breast X-rays for cancer. To apply MCE to this problem, we identify regions of interest (RoI) from the mammograms, divide the RoI into a collection of sub-images and use a chromosome to classify each sub-image. This problem allows us to evaluate various evolutionary mutation operators which can pairwise swap chromosomes either randomly or topographically or reuse chromosomes in place of other chromosomes.     

Combining genetic approach and integer programming to solve multi-facility economic lot-scheduling problem

Economic lot-scheduling problem (ELSP) has been studied since the 1950??s. ELSP deals with the scheduling of the production of several items on a single facility in a cyclical pattern. The facility can only produce one single item at a time, and there is a set-up cost and set-up time associated with each item. Because of the rapid development of many emerging markets nowadays, many common items are produced in different places in order to satisfy the demands in different markets. This becomes the multi-facilities ELSP problems. In ELSP problems, it is known that if more items types to be produced by the facility, the production frequency of each item type will increase because of the balancing of the production rate and the demand rate. Consequently, the number of set-up time and set-up cost increases accordingly. Thus, reallocating the common items, which can be produced in any facilities, to be produced only on certain facility can certainly reduce the number of production frequency, and lead to lower related costs. The objective of this paper is to propose an optimization methodology combining Integer Programming and Genetic Algorithm to solve multi-facility ELSP problems. This paper proposes to divide the main problem into a master problem and sub-problems, which are solved by Integer Programming and Genetic Algorithm respectively. To demonstrate the significance of reallocating the common items and aggregating them to produce in certain facility, several models have been designed and tested. The comparison of the models demonstrates the reduction of the costs benefited by result of common items reallocation.     

基于概率模型的遗传规划在故障诊断中的应用

采用在遗传规划中使用概率模型的新方法采解决一系列故障诊断问题。故障诊断可被看为是一个多级分类问题。遗传规划在解决复杂问题上有很大的优势,而这种优势在故障诊断中仍然显著。而且,使用概率模型作为适应函数能提高诊断的精确性,最后用这种方法解决机电设备的故障诊断。结果显示,使用基于概率模型的遗传规划解决机电设备的故障诊断比人工神经网络优越。     

Breast cancer diagnosis using genetic programming generated feature

This paper proposes a novel method for breast cancer diagnosis using the feature generated by genetic programming (GP). We developed a new feature extraction measure (modified Fisher linear discriminant analysis (MFLDA)) to overcome the limitation of Fisher criterion. GP as an evolutionary mechanism provides a training structure to generate features. A modified Fisher criterion is developed to help GP optimize features that allow pattern vectors belonging to different categories to distribute compactly and disjoint regions. First, the MFLDA is experimentally compared with some classical feature extraction methods (principal component analysis, Fisher linear discriminant analysis, alternative Fisher linear discriminant analysis). Second, the feature generated by GP based on the modified Fisher criterion is compared with the features generated by GP using Fisher criterion and an alternative Fisher criterion in terms of the classification performance. The classification is carried out by a simple classifier (minimum distance classifier). Finally, the same feature generated by GP is compared with a original feature set as the inputs to multi-layer perceptrons and support vector machine. Results demonstrate the capability of this method to transform information from high-dimensional feature space into one-dimensional space and automatically discover the relationship among data, to improve classification accuracy.     

Automated optimum design of structures using genetic programming

Traditionally, the open-domain optimum design of truss structures is solved using conceptual designs which are often based on their ground structures. However, the ground structures are problem-dependent and usually require relatively deep understanding of the problem. This paper presents a genetic programming (GP) based methodology for the automated optimum designs of structures using an approach which is free from ground structures. Thus, it has few requirements about the domain knowledge of the problem and is less problem-dependent. Illustrative example is also presented to show that, compared with genetic algorithm, GP is more flexible and has higher search efficiency when it is employed to solve open-domain structural design problems.     

运用遗传规划解决异或问题的新方法*

介绍了一种用遗传规划这种新的搜索优化技术解决经典异或问题的新途径.遗传规划实质是使用广义的计算机程序来描述问题,并且可以根据环境状况动态改变计算机程序的结构.根据遗传规划特征,引入两种思路、三种方法对异或问题进行求解,取得了很好的效果.与神经网络相比,遗传规划可以动态进化学习并取得显式的数学表达式.     

Predicting high-strength concrete parameters using weighted genetic programming

Genetic programming (GP) is an evolutionary algorithm-based methodology that employs a binary tree topology with optimized functional operators. This study introduced weight coefficients to each GP linkage in a tree in order to create a new weighted genetic programming (WGP) approach. Two distinct advantages of the proposed WGP include (1) balancing the influences of the two front input branches and (2) incorporating weights throughout generated formulas. Resulting formulas contain a certain quantity of optimized functions and weights. Genetic algorithms are employed to accomplish WGP optimization of function selection and proper weighting tasks. Case studies presented herein highlight a high-strength concrete reference study. Results showed that the proposed WGP not only improves GP in terms of introduced weight coefficients, but also provides both accurate results and formula outputs.     

Database query languages and functional logic programming

Functional logic programming is a paradigm which integrates functional and logic programming. It is based on the use of rewriting rules for defining programs, and rewriting for goal solving. In this context, goals, usually, consist of equality (and, sometimes, inequality) constraints, which are solved in order to obtain answers, represented by means of substitutions. On the other hand, database programming languages involve a data model, a data definition language and, finally, a query language against the data defined according to the data model. To use functional logic programming as a database programming language, (1) we will propose a data model involving the main features adopted from functional logic programming (for instance, handling of partial and infinite data), (2) we will use conditional rewriting rules as data definition language, and finally, (3) we will deal with equality and inequality constraints as query language. Moreover, as most database systems, (4) we will propose an extended relational calculus and algebra, which can be used as alternative query languages in this framework. Finally, (5) we will prove that three alternative query languages are equivalent.     

Algebraic query optimisation for database programming languages

A major challenge still facing the designers and implementors of database programming languages (DBPLs) is that of query optimisation. We investigate algebraic query optimisation techniques for DBPLs in the context of a purely declarative functional language that supports sets as first-class objects. Since the language is computationally complete issues such as non-termination of expressions and construction of infinite data structures can be investigated, whilst its declarative nature allows the issue of side effects to be avoided and a richer set of equivalences to be developed. The language has a well-defined semantics which permits us to reason formally about the properties of expressions, such as their equivalence with other expressions and their termination. The support of a set bulk data type enables much prior work on the optimisation of relational languages to be utilised. In the paper we first give the syntax of our archetypal DBPL and briefly discuss its semantics. We then define a small but powerful algebra of operators over the set data type, provide some key equivalences for expressions in these operators, and list transformation principles for optimising expressions. Along the way, we identify some caveats to well-known equivalences for non-deductive database languages. We next extend our language with two higher level constructs commonly found in functional DBPLs: set comprehensions and functions with known inverses. Some key equivalences for these constructs are provided, as are transformation principles for expressions in them. Finally, we investigate extending our equivalences for the set operators to the analogous operators over bags. Although developed and formally proved in the context of a functional language, our findings are directly applicable to other DBPLs of similar expressiveness. Edited by Matthias Jarke, Jorge Bocca, Carlo Zaniolo. Received September 15, 1994 / Accepted September 1, 1995     

Selecting informative data for developing peptide-MHC binding predictors using a query by committee approach

Strategies for selecting informative data points for training prediction algorithms are important, particularly when data points are difficult and costly to obtain. A Query by Committee (QBC) training strategy for selecting new data points uses the disagreement between a committee of different algorithms to suggest new data points, which most rationally complement existing data, that is, they are the most informative data points. In order to evaluate this QBC approach on a real-world problem, we compared strategies for selecting new data points. We trained neural network algorithms to obtain methods to predict the binding affinity of peptides binding to the MHC class I molecule, HLA-A2. We show that the QBC strategy leads to a higher performance than a baseline strategy where new data points are selected at random from a pool of available data. Most peptides bind HLA-A2 with a low affinity, and as expected using a strategy of selecting peptides that are predicted to have high binding affinities also lead to more accurate predictors than the base line strategy. The QBC value is shown to correlate with the measured binding affinity. This demonstrates that the different predictors can easily learn if a peptide will fail to bind, but often conflict in predicting if a peptide binds. Using a carefully constructed computational setup, we demonstrate that selecting peptides with a high QBC performs better than low QBC peptides independently from binding affinity. When predictors are trained on a very limited set of data they cannot be expected to disagree in a meaningful way and we find a data limit below which the QBC strategy fails. Finally, it should be noted that data selection strategies similar to those used here might be of use in other settings in which generation of more data is a costly process.     

Generative learning of visual concepts using multiobjective genetic programming

This paper introduces a novel method of visual learning based on genetic programming, which evolves a population of individuals (image analysis programs) that process attributed visual primitives derived from raw raster images. The goal is to evolve an image analysis program that correctly recognizes the training concept (shape). The approach uses generative evaluation scheme: individuals are rewarded for reproducing the shape of the object being recognized using graphical primitives and elementary background knowledge encoded in predefined operators. Evolutionary run is driven by a multiobjective fitness function to prevent premature convergence and enable effective exploration of the space of solutions. We present the method in detail and verify it experimentally on the task of learning two visual concepts from examples.     

Constitutive modeling of Leighton Buzzard Sands using genetic programming

This paper investigates the results of laboratory experiments and numerical simulations conducted to examine the behavior of mixtures composed of coarse (i.e. Leighton Buzzard Sand fraction B) and fine (i.e. Leighton Buzzard Sand fraction E) sand particles. Emphasis was placed on assessing the role of fines content in mixture and strain level on the deviatoric stress and pore water pressure generation using experimental (i.e. Triaxial testing) and numerical approaches (i.e. genetic programming, GP). The experimental database used for GP modeling is based on a laboratory study of the properties of saturated coarse and fine sand mixtures with various mix ratios under a 100 kPa effective stresses in a 100 mm diameter conventional triaxial testing apparatus. Experimental results show that coarse–fine sand mixtures exhibit clay-like behavior due to particle–particle effects with the increase in fines content. It is shown that GP modeling of coarse–fine sand mixtures is observed to be quite satisfactory. The results have implications in the design of compressible particulate systems and in the development of prediction tools for the field performance coarse–fine sands.     

Morphological algorithm design for binary images using genetic programming

This paper presents a Genetic Programming (GP) approach to the design of Mathematical Morphology (MM) algorithms for binary images. The algorithms are constructed using logic operators and the basic MM operators, i.e. erosion and dilation, with a variety of structuring elements. GP is used to evolve MM algorithms that convert a binary image into another containing just a particular feature of interest. In the study we have tested three fitness functions, training sets with different numbers of elements, training images of different sizes, and 7 different features in two different kinds of applications. The results obtained show that it is possible to evolve good MM algorithms using GP.     

Generating function of color information detection using genetic programming

This article proposes a function for color information detection using genetic programming (GP). In image-processing, object detection is one of the important processes. In cases where the object has a complex color domain, detection becomes more difficult. We generated a detection function for a complex color domain by using GP. The detection function deals with one pixel of an input image, and it obtains an output image by processing for all pixels. We aimed at a reduction in the time taken by a human to consider an image-processing system design. We applied the generation of GP to detect a target color region in actual images. The results show that the detection function has sufficient ability for these detections.