E. Cantu-Paz. A survey of parallel genetic algorithms. Calculateurs Paralleles, Reseaux et Systems Repartis, 10(2):141--171, 1998. Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
Evolving Multiplier Circuits by Training Set and Training .. - Jim Torresen Department (2003) (1 citation) (Correct)
....algorithms (GA) as the string length increases. This often makes the search space too large and explains why only small circuits have been evolvable so far. Thus, work has been undertaken trying to diminish this limitation. Various experiments on speeding up the GA computation have been undertaken [3]. The schemes involve fitness computation in parallel or a partitioned population evolved in parallel. Experiments are focussed on speeding up the GA computation, rather than dividing the application into subtasks. This approach assumes that GA finds a solution if it is allowed to compute enough ....
E. Cantu-Paz. A survey of parallel genetic algorithms. Calculateurs Paralleles, Reseaux et Systems Repartis, 10(2):141--171, 1998.
A Hardware Implementation of a Genetic Programming System Using.. - Martin (2001) (Correct)
....Handel C, I will call this intrinsic paral lelism. The second use of parallelism is in the implementation of the Genetic Programming algorithm. Genetic Algorithms in general are highly parallelisable and exploiting this parallelism can result in substantial performance improvements. Cantu Paz [3] surveyed parallel GA algorithms in depth and proposed four classifications of parallel GA. A uniform taxonomy of parallel Genetic Algorithms has been proposed by Nowostawski and Poli [19] which extended the number of classes of parallel GAs to eight: 11 1. master slave in which a single ....
E. Cantu-Paz. A survey of parallel genetic algorithms. Calculateurs Parallels, Reseaux et Systems Repartis, 10(2):141-171, 1998.
A Cellular Genetic Algorithm Simulating Predator-Prey.. - Li, Sutherland (2002) (Correct)
....as a result of rapid loss of diversity in the GA population, the search is trapped to sub optimal solutions. Many algorithms have been proposed to help maintain a more diverse population so as to prevent premature convergence. Among them, most popular are based on the idea of spatial separation [2,3,4]. In particular cellular GAs (or fine grained Parallel GAs) have been shown to be very effective in maintaining population diversity [5,6,7] In a cellular GA, individuals are commonly mapped onto a 2 dimensional lattice, with each cell corresponding to an individual. Selection and interaction ....
....The prey and its fittest neighbour create a child prey using realcoded crossover and mutation operators (see section 3) After a child prey is created, we use two methods for placing the child on the lattice. These two methods are analogous to the migration schemes often used in an island model [3]. In the first method, the child prey is placed randomly up to two cells away from the parent prey. 10 attempts are tried to place the child, but if no free spot is found, then the child is not generated. The second method simply places the child randomly anywhere on the lattice. Again 10 attempts ....
Cantu-Paz, E. (1997), A Survey of Parallel Genetic Algorithms. Technical Report llliGAL 97003, University of Illinois at Urbana-Champaign.
The Effects of Varying Population Density in a Fine-grained.. - Li, Kirley (2002) (Correct)
.... INTRODUCTION Genetic algorithms (GA) are abstract implementations of natural evolutionary processes used to solve search and optimisation problems [1] 2] Recently, there has been increased interest in parallel versions of the algorithms, in particular where the population has a spatial structure [3] [5] The most common implementations are the coarse grained (or island) model and the fine grained (or grid) model. In a coarse grained model, the GA population is divided into multiple subpopulations (or demes) Each subpopulation evolves independently, with only occasional exchanges of ....
....a natural formation of demes, eliminating the need for pre specifying the size and number of demes. The selection pressure can be also adjusted via the seeding method and migration schemes. Our model is a hybrid parallel GA combining the features of both coarsegrained and a fine grained algorithms [3][5] 15] The motivation for this approach is derived from percolation theory , where the effects of density and formation of clusters on the global behaviour of a system have been the subject of study [17] In this research, we examine the effects of varying population density on the performance ....
Cantu-Paz, E. (1997), A Survey of Parallel Genetic Algorithms. Technical Report IlliGAL 97003, University of Illinois at UrbanaChampaign.
Generational Parallel Varying Mutation GAs and their Applications - Duran (2003) (Correct)
No context found.
E. Cantu-Paz. A survey of parallel genetic algorithms. Calculateurs Paralleles, Reseaux et Systems Repartis, 10(2):141--171, 1998.
Multi-Objective Optimization of Diesel Engine.. - Hiroyasu, Miki.. (2002) (Correct)
No context found.
Cantu-Paz, E., "A survey of parallel genetic algorithms. ", Calculateurs Paralleles, 10(2), 1998.
Performance Comparison - Of Memetic Algorithms (2003) (Correct)
No context found.
E. Cantu-Paz, A survey of parallel genetic algorithms, Calculateurs Paralleles, Reseaux et
Co-Evolving Competitive Behaviours in Genetic Programming - Matkovic (2002) (Correct)
No context found.
E. Cantu-Paz. A survey of parallel genetic algorithms. Calculateurs Paralleles, Reseaux et Systems Repartis, 10(2):141--171, 1998.
A Parallel Memetic Library For Optimization Problems - Digalakis, Margaritis (2002) (Correct)
No context found.
Cantu-Paz E. , "A survey of parallel genetic algorithms",(1997), Calculateurs Paralleles, Reseaux et Systems Repartis, vol 10, number 2, pp. 141-171.
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC