A novel rriethod that allows us to study the emergence of modularity
for genotype-phenotype mapping in the course of Darwinian evolution is described. The evolutionary method used is based on cornposite chromosomes with two parts; One is a binary genotype whereas the other corresponds to the mapping of genes onto phenotype characters. For such generalized chromosomes the modularity is determined by the following intuitive way: The genes are divided into two subgroups; simultaneously with this decomposition also an accompanied decomposition of the set of phenotype characters is defined. We expect that for chromosomes with rnodular structures the genes frorn one group are rnapped onto characters from the respective group, an appearance of “crosslink” mappings is rnaximally suppressed. A fundamental question for the whole evolutionary biology (and also for evolutioriary algorithms and connectionist cognitive science) is the nature of mechanism of evolutionary emergence of modular structures. An idea of effective fitness is used in the presented explanatory simulations. It is based on the rnetaphor of Hinton and Nowlan theory of the Baldwin eífect, and was ušed as an effective idea for generalization of evolutionary algorithms. The effective fitness reflects not only a static concept of the phenotype, but also its ability to be adapted (learned) within a neighborhood of the respective chromosome. The chromosomes determined in the presented paper inay be understood as objects with the type of plasticity. The rnetaphor of the Baldwin effect (or effective fitness) applied to evolutionary algorithms offers an evolutionary tool that is potentially able to produce the emergence of modularity.
The focus of this paper is the application of the genetic programming
framework in the problem of knowledge discovery in databases, more precisely in the task of classification. Genetic programming possesses certain advantages that make it suitable for application in data mining, such as robustness of the algorithm or its convenient structure for rule generation to name a few. This study concentrates on one type of parallel genetic algorithms - cellular (diffusion) model. Emphasis is placed on the improvement of efficiency and scalability of the data mining algorithm, which could be achieved by integrating the algorithm with databases and employing a cellular framework. The cellular model of genetic programming that exploits SQL queries is implemented and applied to the classification task. The results achieve are presented and compared with other machine learning algorithms.
Příspěvek pojednává o využití výpočetní techniky ve fyzikálních vědách, speciálně o aplikaci evolučních algoritmů v problematice deterministického chaosu. V obou ukázkových studiích jsou použity jak klasické evoluční algoritmy, tak algoritmy moderní. Veškeré údaje byly získány mnohonásobně opakovanými simulacemi, jejichž výsledky jasně poukazují na robustnost a životaschopnost použitých metod. Získaná řešení a postupy k nim vedoucí uvedené v článku jsou diskutovány pouze na úrovni informativního charakteru; pro nastudování plného popisu jsou na konci příspěvku uvedeny potřebné odkazy., Ivan Zelinka, Roman Šenkeřík, Zuzana Oplatková., and Obsahuje seznam literatury
This paper deals with the utilizing of the Bayesian optimization algorithm (BOA) for the niultiobjective optimization of combinatorial problems. Three probabilistic models used in the Estimation Distribution Algorithms (EDA), such as UMDA, BMDA and BOA which allow one to search effectively on the promising areas of the combinatorial search space, are discussed. The main attention is focused on the incorporation of Pareto optimality concept into classical structure of the BOA algorithm. We have modified the standard algorithm BOA for one criterion optimization utilizing the known niching techniques to find the Pareto optimal set. The experiments are focused on tree classes of the combinatorial problems: artificial problem with known Pareto set, multiple 0/1 knapsack problém and the bisectioning of hypergraphs as well.
Diabetes mellitus (DM) is a disease affecting millions of people worldwide, and its medical care brings an economic wear to patients and public health systems. Many efforts have been made to deal with DM, one of them is the full-automation of insulin delivery. This idea consists in design a closed-loop control system to maintain blood glucose levels (BGL) within normal ranges. Dynamic models of glucose-insulin-carbohydrates play an important role in synthesis of control algorithms, but also in other aspects of DM care, such as testing glucose sensors, or as support systems for health care decisions. Therefore, there are several mathematical models reproducing glycemic dynamics of DM, most of them validated with nominal parameters of standardized patients. Nevertheless, individual patient-oriented models could open the possibility of having closed-loop personalized therapies. This problem can be addressed through the information provided by open-loop therapy based on continuous glucose monitoring and subcutaneous insulin infusion. This paper considers the problem of identifying particular parameters of a compartmental model of glucose-insulin dynamics in DM; the goal is fitting the model response to historical data of a diabetic patient collected during a time period of her/his daily life. At this time, Sorensen model is one of the most complete compartmental models representing the complex dynamics of the glucose-insulin metabolism. This is a system of 19 ordinary differential equations (ODEs), thus the identification of its parameters is a non-easy task. In this contribution, parameter identification was performed via three evolutionary algorithms: differential evolution, ant colony optimization and particle swarm optimization. The obtained results show that evolutionary algorithms are powerful tools to solve problems of parametric identification. Also, a comparative analysis of the three algorithms was realized throw a wilcoxon sign-rank test, in which colony optimization had the better performance. The model obtained with the estimated parameters could be used to in type 1 diabetes mellitus (T1DM) care, such as in the design of full-automation of insulin infusion.
Logistic Regression (LR) has become a widely used and accepted method to analyze binary or multiclass outcome variables, since it is a flexible tool that can predict probability for the state of a dichotomous variable. A recently proposed LR method is based on the hybridization of a linear model and Evolutionary Product Unit Neural Network (EPUNN) models for binary classification. This produces a high number of coefficients, so two different methods for simplifying the structure of the final model by reducing the number of initial or PU covariates are presented in this paper, both being based on the Wald test. The first method is a Backtracking Backward Search (BBS) method, and the other is similar, but it is based on the standard Simulated Annealing process for the decision steps (SABBS). In this study, we used aerial imagery taken in mid-May to evaluate the potential of two different combinations of LR and EPUNN (LR using PUs (LRPU), as well as LR using Initial covariates and PUs (LRIPU)) and the two presented methods of structural simplification of the final models (BBS and SABBS) used for discriminating Ridolfia segetum patches (one of the most dominant, competitive and persistent weed in sunflower crops) in a naturally infested field of southern Spain. Then, we compared the performance of these methods to six commonly used classification algorithms; our proposals obtaining a competitive performance and a lower number of coefficients.