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

Alternative strategies for asynchronous migration-controlled schemes in parallel genetic algorithms

Migration of individuals allows a fruitful interaction between subpopulations in the island model, a well known distributed approach for evolutionary computing, where separate subpopulations evolve in parallel. This model is well suited for a distributed environment running a Single Program Multiple Data (SPMD) scheme. Here, the same Genetic Algorithm (GA) is replicated in many processors and attempting better convergence, through an expected improvement on genetic diversity, selected individuals are exchanged periodically. For exchanging, an individual is selected from a source subpopulation and then exported towards a target subpopulation. Usually, the imported string is accepted on arrival and then inserted into the target subpopulation. Our earlier experiments on controlled migration showed an improvement on results when contrasted against those obtained by conventional migration approaches. This paper describes extended implementations of alternative strategies to oversee migration in asynchronous schemes for an island model and enlarges a previous work on three processors with a set of softer testing functions [9]. All of them try to decrease the risk of premature convergence. A first strategy attempts to prevent unbalanced propagation of genotypes by applying an acceptance threshold parameter to each incoming string. A second one permits independent evolution of subpopulations and acts only when a possible stagnation is detected. In such condition an attempt to evade falling towards a local optimum is done by inserting an expected dissimilar individual to improve genetic diversity. A third alternative strategy combines both previous mentioned strategies. The results presented are those obtained on the functions that showed to be more difficult for the island model using a replication of a simple GA. A description of the corresponding system architecture supporting the PGA implementation is described and results for the parallel distributed approach among 3, 6 and 12 processors is discussed.Eje: Procesamiento distribuido y paralelo. Tratamiento de señalesRed 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

Alternative strategies for asynchronous migration-controlled schemes in parallel genetic algorithm

Migration of individuals allows a fruitful interaction between subpopulations in the island model, a well known distributed approach for evolutionary computing, where separate subpopulations evolve in parallel. This model is well suited for a distributed environment running a Single Program Multiple Data (SPMD) scheme. Here, the same Genetic Algorithm (GA) is replicated in many processors and attempting better convergence, through an expected improvement on genetic diversity, selected individuals are exchanged periodically. For exchanging, an individual is selected from a source subpopulation and then exported towards a target subpopulation. Usually, the imported string is accepted on arrival and then inserted into the target subpopulation. Our earlier experiments on controlled migration showed an improvement on results when contrasted against those obtained by conventional migration approaches. This paper describes extended implementations of alternative strategies to oversee migration in asynchronous schemes for an island model and enlarges a previous work on three processors with a set of softer testing functions [9]. All of them try to decrease the risk of premature convergence. A first strategy attempts to prevent unbalanced p ropagation of genotypes by applying an acceptance threshold parameter to each incoming string. A second one permits independent evolution of subpopulations and acts only when a possible stagnation is detected. In such condition an attempt to evade falling towards a local optimum is done by inserting an expected d issimilar individual to improve genetic diversity. A third alternative strategy combines both previous mentioned strategies. The results presented are those obtained on the functions that showed to be more difficult for the island model using a replication of a simple GA. A description of the corresponding system architecture supporting the PGA implementation is described and results for the parallel distributed approach among 3, 6 and 12 processors is discussed.Facultad de Informátic

Enhancing evolutionary algorithms through recombination and parallelism

Evolutionary computation (EC) has been recently recognized as a research field, which studies a new type of algorithms: Evolutionary Algorithms (EAs). These algorithms process populations of solutions as opposed to most traditional approaches which improve a single solution. All these algorithms share common features: reproduction, random variation, competition and selection of individuals. During our research it was evident that some components of EAs should be re-examined. Hence, specific topics such as multiple crossovers per couple and its enhancements, multiplicity of parents and crossovers and their application to single and multiple criteria optimization problems, adaptability, and parallel genetic algorithms, were proposed and investigated carefully. This paper show the most relevant and recent enhancements on recombination for a genetic-algorithm-based EA and migration control strategies for parallel genetic algorithms. Details of implementation and results are discussed.I Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

A support for remote process execution in a load-balanced distributed system

Load distribution and balancing in a workstation-based network includes a number of intricate tasks. Among them, transparent remote process execution is an essential one. This work describes the main problems to be considered when implementing remote process execution and propose a design for an alternative system attempting to solve these problems.Eje: Sistemas distribuidosRed de Universidades con Carreras en Informática (RedUNCI

Enhancing evolutionary algorithms through recombination and parallelism

Evolutionary computation (EC) has been recently recognized as a research field, which studies a new type of algorithms: Evolutionary Algorithms (EAs). These algorithms process populations of solutions as opposed to most traditional approaches which improve a single solution. All these algorithms share common features: reproduction, random variation, competition and selection of individuals. During our research it was evident that some components of EAs should be re-examined. Hence, specific topics such as multiple crossovers per couple and its enhancements, multiplicity of parents and crossovers and their application to single and multiple criteria optimization problems, adaptability, and parallel genetic algorithms, were proposed and investigated carefully. This paper show the most relevant and recent enhancements on recombination for a genetic-algorithm-based EA and migration control strategies for parallel genetic algorithms. Details of implementation and results are discussed.Facultad de Informátic

The optimal routing problem in multicomputer networks: an evolutionary approach

Optimal resource allocation is an important issue in computer network administration. One of these problems involves finding an optimal route to transport certain traffic from a source node to a destination node. For messages to get from the sender to the receiver it is necessary to make a number of hops choosing, at each of the intermediate nodes, an outgoing line to use. Selection of an outgoing link can depend on amount of traffic, type of link or other criteria based on the associated cost to each line. The total transportation cost through any of the possible routes is to be minimised. Instead of facing the problem in a step by step decision making fashion, a global approach based on long term averages can be successfully used when network traffic is not extremely dynamic. Given the number of nodes in the network and the interconnection topology this later approach leads to a highly combinatorial problem. Evolutionary Algorithms behave efficiently in searching optimal or near optimal solutions in a wide range of hard combinatorial problems. Moreover, when using an evolutionary approach, instead of a single optimal solution a set of near optimal solutions is provided. This property allows us to provide timely acceptable solutions when the network interconnectivity changes over time. This paper describes a genetic algorithm using a sort of edge crossover, operating on variable length chromosomes. Also a macro-mutation operator is introduced by replacing an entire chromosome to avoid costly repair mechanisms. A report on experiments and results contrasted against conventional approaches is also included.Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

The optimal routing problem in multicomputer networks: an evolutionary approach

Optimal resource allocation is an important issue in computer network administration. One of these problems involves finding an optimal route to transport certain traffic from a source node to a destination node. For messages to get from the sender to the receiver it is necessary to make a number of hops choosing, at each of the intermediate nodes, an outgoing line to use. Selection of an outgoing link can depend on amount of traffic, type of link or other criteria based on the associated cost to each line. The total transportation cost through any of the possible routes is to be minimised. Instead of facing the problem in a step by step decision making fashion, a global approach based on long term averages can be successfully used when network traffic is not extremely dynamic. Given the number of nodes in the network and the interconnection topology this later approach leads to a highly combinatorial problem. Evolutionary Algorithms behave efficiently in searching optimal or near optimal solutions in a wide range of hard combinatorial problems. Moreover, when using an evolutionary approach, instead of a single optimal solution a set of near optimal solutions is provided. This property allows us to provide timely acceptable solutions when the network interconnectivity changes over time. This paper describes a genetic algorithm using a sort of edge crossover, operating on variable length chromosomes. Also a macro-mutation operator is introduced by replacing an entire chromosome to avoid costly repair mechanisms. A report on experiments and results contrasted against conventional approaches is also included.Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI

Enhancing evolutionary algorithms through recombination and parallelism

Evolutionary computation (EC) has been recently recognized as a research field, which studies a new type of algorithms: Evolutionary Algorithms (EAs). These algorithms process populations of solutions as opposed to most traditional approaches which improve a single solution. All these algorithms share common features: reproduction, random variation, competition and selection of individuals. During our research it was evident that some components of EAs should be re-examined. Hence, specific topics such as multiple crossovers per couple and its enhancements, multiplicity of parents and crossovers and their application to single and multiple criteria optimization problems, adaptability, and parallel genetic algorithms, were proposed and investigated carefully. This paper show the most relevant and recent enhancements on recombination for a genetic-algorithm-based EA and migration control strategies for parallel genetic algorithms. Details of implementation and results are discussed.Facultad de Informátic