Genetic algorithms

Genetic algorithms are mathematical, highly parallel, adaptive search procedures (i.e., problem solving methods) based loosely on the processes of natural genetics and Darwinian survival of the fittest. Basic genetic algorithms concepts are introduced, genetic algorithm applications are introduced, and results are presented from a project to develop a software tool that will enable the widespread use of genetic algorithm technology

Coarse-grained parallel genetic algorithms: Three implementations and their analysis

Although solutions to many problems can be found using direct analytical methods such as those calculus provides, many problems simply are too large or too difficult to solve using traditional techniques. Genetic algorithms provide an indirect approach to solving those problems. A genetic algorithm applies biological genetic procedures and principles to a randomly generated collection of potential solutions. The result is the evolution of new and better solutions. Coarse-Grained Parallel Genetic Algorithms extend the basic genetic algorithm by introducing genetic isolation and distribution of the problem domain. This thesis compares the capabilities of a serial genetic algorithm and three coarse-grained parallel genetic algorithms (a standard parallel algorithm, a non-uniform parallel algorithm and an adaptive parallel algorithm). The evaluation is done using an instance of the traveling salesman problem. It is shown that while the standard course-grained parallel algorithm provides more consistent results than the serial genetic algorithm, the adaptive distributed algorithm out-performs them both. To facilitate this analysis, an extensible object-oriented library for genetic algorithms, encompassing both serial and coarse-grained parallel genetic algorithms, was developed. The Java programming language was used throughout

A Survey of Parallel Data Mining

With the fast, continuous increase in the number and size of databases, parallel data mining is a natural and cost-effective approach to tackle the problem of scalability in data mining. Recently there has been a considerable research on parallel data mining. However, most projects focus on the parallelization of a single kind of data mining algorithm/paradigm. This paper surveys parallel data mining with a broader perspective. More precisely, we discuss the parallelization of data mining algorithms of four knowledge discovery paradigms, namely rule induction, instance-based learning, genetic algorithms and neural networks. Using the lessons learned from this discussion, we also derive a set of heuristic principles for designing efficient parallel data mining algorithms

Parallel genetic algorithms: a feasible distributed : Implementation

Parallel genetic algorithms, models and implementations, attempts to exploit the intrinsically parallel nature of genetic algorithms. By distributing the total population, these models ref1ects a bebaviour nearer to that of natural systems. A variety of parallel computer systems architectures can offer distinct support features for their implementation. Ibis paper shows sorne remarkable characteristics of parallel genetic algorithms, details of a feasible design and their implementation. A1so some results related to the island model are shown.Eje: Redes Neuronales. Algoritmos genéticosRed de Universidades con Carreras en Informática (RedUNCI

Parallel genetic algorithms: a feasible distributed : Implementation

Parallel genetic algorithms, models and implementations, attempts to exploit the intrinsically parallel nature of genetic algorithms. By distributing the total population, these models ref1ects a bebaviour nearer to that of natural systems. A variety of parallel computer systems architectures can offer distinct support features for their implementation. Ibis paper shows sorne remarkable characteristics of parallel genetic algorithms, details of a feasible design and their implementation. A1so some results related to the island model are shown.Eje: Redes Neuronales. Algoritmos genéticosRed de Universidades con Carreras en Informática (RedUNCI

Parallel Genetic Algorithms with GPU Computing

Genetic algorithms (GAs) are powerful solutions to optimization problems arising from manufacturing and logistic fields. It helps to find better solutions for complex and difficult cases, which are hard to be solved by using strict optimization methods. Accelerating parallel GAs with GPU computing have received significant attention from both practitioners and researchers, ever since the emergence of GPU-CPU heterogeneous architectures. Designing a parallel algorithm on GPU is different fundamentally from designing one on CPU. On CPU architecture, typically data or tasks are distributed across tens of threads or processes, while on GPU architecture, more than hundreds of thousands of threads run. In order to fully utilize the computing power of GPUs, the design approaches and implementation strategies of parallel GAs should be re-probed. In the chapter, a concise overview of parallel GAs on GPU is given from the perspective of GPU architecture. The concept of parallelism granularity is redefined, the aspect of data layout is discussed on how it will affect the kernel performance, and the hierarchy of threads is examined on how threads are organized in the grid and blocks to expose sufficient parallelism to GPU. Some future research is discussed. A hybrid parallel model, based on the feature of GPU architecture, is suggested to build up efficient parallel GAs for hyper-scale problems

A GPU-Computing Approach to Solar Stokes Profile Inversion

We present a new computational approach to the inversion of solar photospheric Stokes polarization profiles, under the Milne-Eddington model, for vector magnetography. Our code, named GENESIS (GENEtic Stokes Inversion Strategy), employs multi-threaded parallel-processing techniques to harness the computing power of graphics processing units GPUs, along with algorithms designed to exploit the inherent parallelism of the Stokes inversion problem. Using a genetic algorithm (GA) engineered specifically for use with a GPU, we produce full-disc maps of the photospheric vector magnetic field from polarized spectral line observations recorded by the Synoptic Optical Long-term Investigations of the Sun (SOLIS) Vector Spectromagnetograph (VSM) instrument. We show the advantages of pairing a population-parallel genetic algorithm with data-parallel GPU-computing techniques, and present an overview of the Stokes inversion problem, including a description of our adaptation to the GPU-computing paradigm. Full-disc vector magnetograms derived by this method are shown, using SOLIS/VSM data observed on 2008 March 28 at 15:45 UT