A genetic approach using direct representation of solution for the parallel task scheduling problem

In scheduling, a set of machines in parallel is a setting that is important, from both the theoretical and practical points of view. From the theoretical viewpoint, it is a generalization of the single machine scheduling problem. From the practical point of view the occurrence of resources in parallel is common in real-world. When machines are computers, a parallel program can be conceived as a set of parallel components (tasks) which can be executed according to some precedence relationship. In this case efficient scheduling of tasks permits to take full advantage of the computational power provided by a multiprocessor or a multicomputer system. This kind of planning involves the assignment of partially ordered tasks onto the system architecture processing components. This paper shows the problem of allocating a number of non-identical tasks in a multiprocessor or multicomputer system. The model assumes that the system consists of a number of identical processors and only one task may execute on a processor at a time. All schedules and tasks are non-preemptive. The well-known Graham’s list scheduling algorithm (LSA) is contrasted with an evolutionary approach using a direct representation of solutions.Eje: Computación evolutivaRed de Universidades con Carreras en Informática (RedUNCI

A genetic approach using direct representation of solution for the parallel task scheduling problem

In scheduling, a set of machines in parallel is a setting that is important, from both the theoretical and practical points of view. From the theoretical viewpoint, it is a generalization of the single machine scheduling problem. From the practical point of view the occurrence of resources in parallel is common in real-world. When machines are computers, a parallel program can be conceived as a set of parallel components (tasks) which can be executed according to some precedence relationship. In this case efficient scheduling of tasks permits to take full advantage of the computational power provided by a multiprocessor or a multicomputer system. This kind of planning involves the assignment of partially ordered tasks onto the system architecture processing components. This paper shows the problem of allocating a number of non-identical tasks in a multiprocessor or multicomputer system. The model assumes that the system consists of a number of identical processors and only one task may execute on a processor at a time. All schedules and tasks are non-preemptive. The well-known Graham’s list scheduling algorithm (LSA) is contrasted with an evolutionary approach using a direct representation of solutions.Eje: Computación evolutivaRed de Universidades con Carreras en Informática (RedUNCI

Asignación de tareas a procesadores en un sistema distribuido de tiempo real duro utilizando algoritmos genéticos y lógica difusa

Se presenta un método basado en algoritmos genéticos para atacar el problema de asignación de un conjunto de tareas apropiativas, sobre un conjunto de procesadores distribuidos que deben trabajar en un entorno de tiempo real duro. Las tareas son cooperativas y utilizan como vía de comunicación una red local. Los coeficientes que ponderan la función de costo del algoritmo genético son calculados utilizando operadores difusos. Sobre el sistema existe un conjunto de restricciones que debe ser satisfecho para obtener una solución compatible con los requerimientos de tiempo real duro.Eje: Workshop sobre Aspectos Teoricos de la Inteligencia ArtificialRed de Universidades con Carreras en Informática (RedUNCI

Asignación de tareas a procesadores en un sistema distribuido de tiempo real duro utilizando algoritmos genéticos y lógica difusa

Se presenta un método basado en algoritmos genéticos para atacar el problema de asignación de un conjunto de tareas apropiativas, sobre un conjunto de procesadores distribuidos que deben trabajar en un entorno de tiempo real duro. Las tareas son cooperativas y utilizan como vía de comunicación una red local. Los coeficientes que ponderan la función de costo del algoritmo genético son calculados utilizando operadores difusos. Sobre el sistema existe un conjunto de restricciones que debe ser satisfecho para obtener una solución compatible con los requerimientos de tiempo real duro.Eje: Workshop sobre Aspectos Teoricos de la Inteligencia ArtificialRed de Universidades con Carreras en Informática (RedUNCI

Asignación de tareas a procesadores en un sistema distribuido de tiempo real duro utilizando algoritmos genéticos y lógica difusa

Se presenta un método basado en algoritmos genéticos para atacar el problema de asignación de un conjunto de tareas apropiativas, sobre un conjunto de procesadores distribuidos que deben trabajar en un entorno de tiempo real duro. Las tareas son cooperativas y utilizan como vía de comunicación una red local. Los coeficientes que ponderan la función de costo del algoritmo genético son calculados utilizando operadores difusos. Sobre el sistema existe un conjunto de restricciones que debe ser satisfecho para obtener una solución compatible con los requerimientos de tiempo real duro.Eje: Workshop sobre Aspectos Teoricos de la Inteligencia ArtificialRed de Universidades con Carreras en Informática (RedUNCI

A new load balancing heuristic using self-organizing maps

Ankara : The Department of Computer Engineering and Information Science and the Institute of Engineering and Science of Bilkent Univ., 1999.Thesis (Master's) -- Bilkent University, 1999.Includes bibliographical references leaves 68-71.Atun, MuratM.S

A grammar-based technique for genetic search and optimization

The genetic algorithm (GA) is a robust search technique which has been theoretically and empirically proven to provide efficient search for a variety of problems. Due largely to the semantic and expressive limitations of adopting a bitstring representation, however, the traditional GA has not found wide acceptance in the Artificial Intelligence community. In addition, binary chromosones can unevenly weight genetic search, reduce the effectiveness of recombination operators, make it difficult to solve problems whose solution schemata are of high order and defining length, and hinder new schema discovery in cases where chromosome-wide changes are required.;The research presented in this dissertation describes a grammar-based approach to genetic algorithms. Under this new paradigm, all members of the population are strings produced by a problem-specific grammar. Since any structure which can be expressed in Backus-Naur Form can thus be manipulated by genetic operators, a grammar-based GA strategy provides a consistent methodology for handling any population structure expressible in terms of a context-free grammar.;In order to lend theoretical support to the development of the syntactic GA, the concept of a trace schema--a similarity template for matching the derivation traces of grammar-defined rules--was introduced. An analysis of the manner in which a grammar-based GA operates yielded a Trace Schema Theorem for rule processing, which states that above-average trace schemata containing relatively few non-terminal productions are sampled with increasing frequency by syntactic genetic search. Schemata thus serve as the building blocks in the construction of the complex rule structures manipulated by syntactic GAs.;As part of the research presented in this dissertation, the GEnetic Rule Discovery System (GERDS) implementation of the grammar-based GA was developed. A comparison between the performance of GERDS and the traditional GA showed that the class of problems solvable by a syntactic GA is a superset of the class solvable by its binary counterpart, and that the added expressiveness greatly facilitates the representation of GA problems. to strengthen that conclusion, several experiments encompassing diverse domains were performed with favorable results