Constraints in Genetic Programming

Genetic programming refers to a class of genetic algorithms utilizing generic representation in the form of program trees. For a particular application, one needs to provide the set of functions, whose compositions determine the space of program structures being evolved, and the set of terminals, which determine the space of specific instances of those programs. The algorithm searches the space for the best program for a given problem, applying evolutionary mechanisms borrowed from nature. Genetic algorithms have shown great capabilities in approximately solving optimization problems which could not be approximated or solved with other methods. Genetic programming extends their capabilities to deal with a broader variety of problems. However, it also extends the size of the search space, which often becomes too large to be effectively searched even by evolutionary methods. Therefore, our objective is to utilize problem constraints, if such can be identified, to restrict this space. In this publication, we propose a generic constraint specification language, powerful enough for a broad class of problem constraints. This language has two elements -- one reduces only the number of program instances, the other reduces both the space of program structures as well as their instances. With this language, we define the minimal set of complete constraints, and a set of operators guaranteeing offspring validity from valid parents. We also show that these operators are not less efficient than the standard genetic programming operators if one preprocesses the constraints - the necessary mechanisms are identified

Adaptable Constrained Genetic Programming: Extensions and Applications

An evolutionary algorithm applies evolution-based principles to problem solving. To solve a problem, the user defines the space of potential solutions, the representation space. Sample solutions are encoded in a chromosome-like structure. The algorithm maintains a population of such samples, which undergo simulated evolution by means of mutation, crossover, and survival of the fittest principles. Genetic Programming (GP) uses tree-like chromosomes, providing very rich representation suitable for many problems of interest. GP has been successfully applied to a number of practical problems such as learning Boolean functions and designing hardware circuits. To apply GP to a problem, the user needs to define the actual representation space, by defining the atomic functions and terminals labeling the actual trees. The sufficiency principle requires that the label set be sufficient to build the desired solution trees. The closure principle allows the labels to mix in any arity-consistent manner. To satisfy both principles, the user is often forced to provide a large label set, with ad hoc interpretations or penalties to deal with undesired local contexts. This unfortunately enlarges the actual representation space, and thus usually slows down the search. In the past few years, three different methodologies have been proposed to allow the user to alleviate the closure principle by providing means to define, and to process, constraints on mixing the labels in the trees. Last summer we proposed a new methodology to further alleviate the problem by discovering local heuristics for building quality solution trees. A pilot system was implemented last summer and tested throughout the year. This summer we have implemented a new revision, and produced a User's Manual so that the pilot system can be made available to other practitioners and researchers. We have also designed, and partly implemented, a larger system capable of dealing with much more powerful heuristics

CGP lil-gp 2.1;1.02 User's Manual

This document describes extensions provided to lil-gp facilitating dealing with constraints. This document deals specifically with lil-gp 1.02, and the resulting extension is referred to as CGP lil-gp 2.1; 1.02 (the first version is for the extension, the second for the utilized lil-gp version). Unless explicitly needed to avoid confusion, version numbers are omitted

Forecasting time series by means of evolutionary algorithms

Proceeding of: 8th International Conference in Parallel Problem Solving from Nature - PPSN VIII , Birmingham, UK, September 18-22, 2004.The time series forecast is a very complex problem, consisting in predicting the behaviour of a data series with only the information of the previous sequence. There is many physical and artificial phenomenon that can be described by time series. The prediction of such phenomenon could be very complex. For instance, in the case of tide forecast, unusually high tides, or sea surges, result from a combination of chaotic climatic elements in conjunction with the more normal, periodic, tidal systems associated with a particular area. Too much variables influence the behaviour of the water level. Our problem is not only to find prediction rules, we also need to discard the noise and select the representative data. Our objective is to generate a set of prediction rules. There are many methods tying to achieve good predictions. In most of the cases this methods look for general rules that are able to predict the whole series. The problem is that usually the time series has local behaviours that dont allow a good level of prediction when using general rules. In this work we present a method for finding local rules able to predict only some zones of the series but achieving better level prediction. This method is based on the evolution of set of rules genetically codified, and following the Michigan approach. For evaluating the proposal, two different domains have been used: an artificial domain widely use in the bibliography (Mackey-Glass series) and a time series corresponding to a natural phenomenon, the water level in Venice Lagoon.Investigation supported by the Spanish Ministry of Science and Technology through the TRACER project under contract TIC2002-04498-C05-

Efficient Reduced-Bias Genetic Algorithm (ERBGA) for Generic Community Detection Objectives

Community structure identification has been an important research area for biology, physics, information systems, and social sciences for studying properties of networks representing complex relationships. Lately, Genetic Algorithms (GAs) are being utilized for community detection. GAs are machine-learning methods that mimic natural selection. However, previous approaches suffer from some deficiencies: redundant representation and linearity assumption, that we will try to address. in. The algorithm presented here is a novel framework that addresses both of these above issues. This algorithm is also flexible as it is easily adapted to any given mathematical objective. Additionally, our approach doesn’t require prior information about the number of true communities in the network. Overall, our efficient approach holds potential for sifting out communities representing complex relationships in networks of interest across different domains

Useful of Tokyo Guidelines in the diagnosis of acute cholecystitis. Anatomopathologie correlationship

ABSTRACTBackground: In the year 2007 a group of experts come together to discuss criteria for acute cholecystitis and to establish therapeutic guidelines and states of gravity in this disease. Objectives: we correlated the criteria of the Tokyo Guidelines 2007 with the anatomopathology study of the surgical specimen.Setting: Service of Urgencies of the National Clinic Hospital in Córdoba, Argentine.Methods: We studied 324 patients (120 male and 204 female) older than 15 years and without limits of age with the criteria of acute cholecystitis a cord to the Tokyo guidelines 2007. 202 patients had a preoperative diagnosis of chronic cholecystitis and 89 of acute cholecystitis, all received cholecystectomy and studied the operative specimen in the anatomopathology department. Anatomopathology criteria for acute cholecystitis were the presence of polimorpho nuclear cells (PMN), for acute exacerbation of chronic cholecystitis the presence of PMN and monomorpho nuclear cells (MN), and for chronic cholecystitis the presence of MN with or without fibrosis.Results: This work showed 82,14% of sensitivity for the diagnostic criteria of Tokyo guidelines, 74,03% of specificity, and positive predictive value of 46%. With the Bayes Theorem the predictive value in Córdoba city was 18,49%.Conclusion: There is an important difference in the specificity and positive predictive value between our work and the Tokyo guidelines for acute cholecystitis. There is an important group of patients in our work with acute exacerbation of chronic cholecystitis that is not classified in the diagnostic criteria for acute cholecystitis of Tokyo guidelines. UTILIDAD DE LAS GUIAS DE TOKIO EN LA COLECISTITIS AGUDA. Correlación Anatomopatológica (Largo) GUIAS DE TOKIO Y COLECISTITIS AGUDA (Corto) RESUMENIntroducción: En el año 2007 un grupo de expertos reunidos en Tokio han ideado los criterios para el diagnóstico de colecistitis aguda con el objeto de establecer pautas terapéuticas e idear grados de gravedad en esta patología.Material y Método: Se estudiaron 324 pacientes (120 masculinos y 204 femeninos) mayores de 15 años sin límite de edad con los criterios diagnósticos para colecistitis aguda según las guías de Tokio 2007. Se diferenciaron 202 pacientes con diagnóstico preoperatorio de colecistitis crónica litiásica y 89 con diagnóstico de colecistitis aguda, todos sometidos a colecistectomía y a posterior se estudiaron las piezas operatorias en el departamento de anatomía patológica utilizando como criterios de colecistitis aguda la presencia de células polimorfo nucleares (PMN), de colecistitis crónica reagudizada la presencia de PMN mas mononucleares (MN), y para colecistitis crónica la presencia de MN con o sin focos de fibrosis.Resultados: El estudio mostró una sensibilidad del 82,14% para los criterios diagnósticos de las guías de Tokio, una especificidad del 74,03% y un valor predictivo positivo del 46%. Aplicando el Teorema de Bayes para analizar el valor predictivo, se obtuvo que el mismo sea del 18,49% en nuestra comunidad.Conclusiones: Existe una notoria diferencia en cuanto a la especificidad y valor predictivo positivo de los criterios de diagnóstico de CA establecidos en las guías de Tokio y las obtenidas en nuestro trabajo. Existe un grupo importante de pacientes con diagnóstico de enfermedad crónica reagudizada que no es clasificada en los trabajos de Tokio.</p

Efficient Distributed Decision Trees for Robust Regression

The availability of massive volumes of data and recent advances in data collection and processing platforms have motivated the development of distributed machine learning algorithms. In numerous real-world applications large datasets are inevitably noisy and contain outliers. These outliers can dramatically degrade the performance of standard machine learning approaches such as regression trees. To this end, we present a novel distributed regression tree approach that utilizes robust regression statistics, statistics that are more robust to outliers, for handling large and noisy data. We propose to integrate robust statistics based error criteria into the regression tree. A data summarization method is developed and used to improve the efficiency of learning regression trees in the distributed setting. We implemented the proposed approach and baselines based on Apache Spark, a popular distributed data processing platform. Extensive experiments on both synthetic and real datasets verify the effectiveness and efficiency of our approach

Discovery of potent, novel, non-toxic anti-malarial compounds via quantum modelling, virtual screening and in vitro experimental validation

<p>Abstract</p> <p>Background</p> <p>Developing resistance towards existing anti-malarial therapies emphasize the urgent need for new therapeutic options. Additionally, many malaria drugs in use today have high toxicity and low therapeutic indices. Gradient Biomodeling, LLC has developed a quantum-model search technology that uses quantum similarity and does not depend explicitly on chemical structure, as molecules are rigorously described in fundamental quantum attributes related to individual pharmacological properties. Therapeutic activity, as well as toxicity and other essential properties can be analysed and optimized simultaneously, independently of one another. Such methodology is suitable for a search of novel, non-toxic, active anti-malarial compounds.</p> <p>Methods</p> <p>A set of innovative algorithms is used for the fast calculation and interpretation of electron-density attributes of molecular structures at the quantum level for rapid discovery of prospective pharmaceuticals. Potency and efficacy, as well as additional physicochemical, metabolic, pharmacokinetic, safety, permeability and other properties were characterized by the procedure. Once quantum models are developed and experimentally validated, the methodology provides a straightforward implementation for lead discovery, compound optimizzation and <it>de novo </it>molecular design.</p> <p>Results</p> <p>Starting with a diverse training set of 26 well-known anti-malarial agents combined with 1730 moderately active and inactive molecules, novel compounds that have strong anti-malarial activity, low cytotoxicity and structural dissimilarity from the training set were discovered and experimentally validated. Twelve compounds were identified <it>in silico </it>and tested <it>in vitro</it>; eight of them showed anti-malarial activity (IC50 ≤ 10 μM), with six being very effective (IC50 ≤ 1 μM), and four exhibiting low nanomolar potency. The most active compounds were also tested for mammalian cytotoxicity and found to be non-toxic, with a therapeutic index of more than 6,900 for the most active compound.</p> <p>Conclusions</p> <p>Gradient's metric modelling approach and electron-density molecular representations can be powerful tools in the discovery and design of novel anti-malarial compounds. Since the quantum models are agnostic of the particular biological target, the technology can account for different mechanisms of action and be used for <it>de novo </it>design of small molecules with activity against not only the asexual phase of the malaria parasite, but also against the liver stage of the parasite development, which may lead to true causal prophylaxis.</p