ABSTRACT
Statistical and genetic programming techniques have been used to predict the software development effort of large software projects. In this paper, a genetic programming model was used for predicting the effort required in individually developed projects. Accuracy obtained from a genetic programming model was compared against one generated from the application of a statistical regression model. A sample of 219 projects developed by 71 practitioners was used for generating the two models, whereas another sample of 130 projects developed by 38 practitioners was used for validating them. The models used two kinds of lines of code as well as programming language experience as independent variables. Accuracy results from the model obtained with genetic programming suggest that it could be used to predict the software development effort of individual projects when these projects have been developed in a disciplined manner within a development-controlled environment.
Subject(s)
Algorithms , Evolution, Molecular , Software , Linear Models , Models, Genetic , Reproducibility of ResultsABSTRACT
Cell differentiation has a crucial role in both artificial and natural developments. This paper presents results from simulations in which a genetic algorithm (GA) was used to evolve artificial regulatory networks (ARNs) to produce predefined 3D cellular structures through the selective activation and inhibition of genes. The ARNs used in this work are extensions of a model previously used to create 2D geometrical patterns. The GA worked by evolving the gene regulatory networks that were used to control cell reproduction, which took place in a testbed based on cellular automata (CA). After the final chromosomes were produced, a single cell in the middle of the CA lattice was allowed to replicate controlled by the ARN found by the GA, until the desired cellular structures were formed. Two simple cubic layered structures were first developed to test multiple gene synchronization. The model was then applied to the problem of generating a 3D French flag pattern using morphogenetic gradients to provide cells with positional information that constrained cellular replication.
Subject(s)
Cell Differentiation , Gene Regulatory Networks , Models, Theoretical , Algorithms , MorphogenesisABSTRACT
We used functional magnetic resonance imaging (fMRI) to identify brain regions involved in the process of mapping coherent discourse onto a developing mental representation. We manipulated discourse coherence by presenting sentences with definite articles (which lead to more coherent discourse) or indefinite articles (which lead to less coherent discourse). Comprehending connected discourse, compared with reading unrelated sentences, produced more neural activity in the right than left hemisphere of the frontal lobe. Thus, the right hemisphere of the frontal lobe is involved in some of the processes underlying mapping. In contrast, left-hemisphere structures were associated with lower-level processes in reading (such as word recognition and syntactic processing). Our results demonstrate the utility of using fMRI to investigate the neural substrates of higher-level cognitive processes such as discourse comprehension.
