Genetic Programming as Alternative for Predicting Development Effort of Individual Software Projects

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

Evolving cellular automata for form generation in artificial development

Form generation or morphogenesis has a crucial role in both artificial and natural development. This chapter presents results from simulations in which a genetic algorithm (GA) was used to evolve cellular automata (CA) in order to generate predefined 2D and 3D shapes. The 2D shapes initially considered were a square, a diamond, a triangle and a circle, whereas for the 3D case the shapes chosen were a cube and a sphere. The CA's rule was defined as a lookup table where the input was determined by the interaction neighborhood's cell state values, and the output established whether or not a cell was to be reproduced at the empty objective cell. Four different 2D interaction neighborhoods were considered: von Neumann, Moore, 2-Radial, and Margolus; a 3D Margolus neighborhood was used to generate the sphere and the cube. In all cases, the GA worked by evolving the chromosomes consisting of the CA rule table's output bits and a section of bits coding for the number of iterations that the model was to run. After the final chromosomes were obtained for all shapes, the CA model was allowed to run starting with a single cell in the middle of the lattice until the allowed number of iterations was reached and a shape was formed. The transition rules that formed some of these basic shapes and others were later combined with an Artificial (gene) Regulatory Network (ARN) to make up genomes that controlled the activation sequence of the CA's rules to generate predefined patterns. The ARN was also evolved by a GA in order to produce cell patterns through the selective activation and inhibition of genes. Morphogenetic gradients were used to provide cells with positional information that constrained cellular replication. After a genome was evolved, a single cell in the middle of the CA lattice was allowed to reproduce until a desired cell pattern consisting of the combination of basic forms was generated. © 2011 Nova Science Publishers, Inc

Artificial development

Artificial Development is a field of Evolutionary Computation inspired by the developmental processes and cellular growth seen in nature. Multiple models of artificial development have been proposed in the past, which can be broadly divided into those based on biochemical processes and those based on a high level grammar. Two of the most important aspects to consider when designing a cellular growth model are the type of representation used to specify the final features of the system, and the abstraction level necessary to capture the properties to be modeled. Although advances in this field have been significant, there is much knowledge to be gained before a model that approaches the level of complexity found in living organisms can be built. Zapotitlán 2009 Springer-Verlag Berlin Heidelberg

Evolving an artificial regulatory network for 2D cell patterning

Cell pattern formation has a central role in both artificial and natural development. This paper provides results from experiments In which a genetic algorithm (GA) was used to evolve an artificial regulatory network (ARN) to produce predefined bidimensional cell patterns through the selective activation of genes. The GA worked by evolving the gene regulatory network that was used to control cell reproduction. Cellular automata (CA) were chosen as models for cell patterning. After the final chromosomes were obtained, a single cell In the middle of the CA lattice was allowed to reproduce controlled by the ARN found by the GA, until the desired pattern was formed. © 2007 IEEE

Use of a genetic algorithm to evolve an extended artificial regulatory network for cell pattern generation

Cell pattern formation has a crucial role in both artificial and natural development. We present results from experiments in which a genetic algorithm was used to evolve an extended artificial regulatory network to produce predefined 2D cell patterns through the selective activation and inhibition of genes. Copyright 2007 ACM

Performance comparison of three topologies of the Island model of a parallel genetic algorithm implementation on a cluster platform

Parallel genetic algorithms (PGAs) have been used to improve the potential of genetic algorithms, which are efficient search techniques that have been employed in producing satisfactory solutions to optimization problems in which the application of standard techniques is not feasible or recommended. We implemented three communication topologies of the island model of a PGA, with the aim of analyzing the performance of the migration models applied. The topologies implemented were the star, the unidirectional ring and the bidirectional ring topologies. The implementation was developed on a cluster platform through the use of the standard Message Passing Interface (MPI) library. In order to test the performance of the algorithm with its variants, well-known benchmark functions were used. © 2012 IEEE

Percutaneous renal access: The learning curve of a simplified approach

Parallel genetic algorithms (PGAs) have been used to improve the potential of genetic algorithms, which are efficient search techniques that have been employed in producing satisfactory solutions to optimization problems in which the application of standard techniques is not feasible or recommended. We implemented three communication topologies of the island model of a PGA, with the aim of analyzing the performance of the migration models applied. The topologies implemented were the star, the unidirectional ring and the bidirectional ring topologies. The implementation was developed on a cluster platform through the use of the standard Message Passing Interface (MPI) library. In order to test the performance of the algorithm with its variants, well-known benchmark functions were used. " 2012 IEEE.",,,,,,"10.1109/CONIELECOMP.2012.6189871",,,"http://hdl.handle.net/20.500.12104/43532","http://www.scopus.com/inward/record.url?eid=2-s2.0-84862061690&partnerID=40&md5=1bedc17aaefee0d3d82cca8c802da77b",,,,,,,,"CONIELECOMP 2012 - 22nd International Conference on Electronics Communications and Computing",,"

3D cell pattern generation in artificial development

Cell pattern formation has an important role in both artificial and natural development. This paper presents an artificial development model for 3D cell pattern generation based on the cellular automata paradigm. Cell replication is controlled by a genome consisting of an artificial regulatory network and a series of structural genes. The genome was evolved by a genetic algorithm in order to generate 3D cell patterns through the selective activation and inhibition of genes.Morphogenetic gradients were used to provide cells with positional information that constrained cellular replication in space. The model was applied to the problem of growing a solid French flag pattern in a 3D virtual space. © 2010 Springer-Verlag Berlin Heidelberg