E. Alba and J.M. Troya
En este trabajo caracterizamos a los algoritmos genéticos tanto secuenciales como paralelos de grano grueso. Para solucionar algunos de los problemas respecto a las implementaciones distribuidas tradicionales se propone una nueva clase de algoritmos genéticos distribuidos llamados Incrementales y se discuten diferentes aspectos de su diseño e implementación así como las ventajas derivadas de su uso.
Samir W. Mahfoud
This paper presents simple analytical models of GAs which are used in multimodal function optimization. The methodology for constructing the models is similar throughout the study. The predictive value of each model is veried by running the corresponding GA on various multimodal functions of varying complexity.
E. Alba and J.M. Troya
Non-parallel GAS can be classified into two classes: panmictic and structured-population algorithms. Our aim is to extend the existing studies from more conventional sequential islands to other kinds of evolution. Also, observations on the relative performance of the different models are offered.
E. Alba, J.F. Aldana and J.M. Troya
Datalog is a query language for deductive databases. This language allows to evaluate a query incrementally on a network of processes. The tuples flow among them in parallel in order to compute the solution to the query. A major issue in this evaluation is the problem of assigning the processes to processors in a multiprocessors system. We have tackled this problem by using a GA that works on a specific fitness function that allows to meet these goals.
Jonathan L. Shapiro and Adam Prugel-Bennett
A maximum entropy approach is used to derive a set of equations describing the evolution of a genetic algorithm involving crossover, mutation and selection. The problem is formulated in terms of cumulants of the fitness distribution.
Pedro Cuesta, Blas Galván, David Greiner and Gabriel Winter
El objetivo del trabajo es incrementar la eficiencia computacional por reducción del tratamiento de la función objetivo introduciendo un patrón en el esquema que se acerque a la solución deseada.Con ello se reduce el 80% de computación.
Rainer Menke
This article gives an example who shows why the schema theorem gives a wrong estimate. Based on a modeling which allows a mathematical analysis of genetic algorithms, the schema theorem is revised and a corrected estimate is shown.
Thomas Back and Dirk Wiesmann
The dynamic environment requires the evolutionary algorithm to maintain sufficient diversity for a continuous adaptation to the changes of the landscape. We present some technical details about dynamic environments and the self-adaptation principle.
Kalyanmoy Deb and Samir Agrawal
In this paper, we investigate the performance of simple tripartite GAs on a number of simple to complex test problems from a practical standpoint. We compare different GAs for a fixed number of function evaluations.
Jim Smith and Frank Vavak
This paper investigates the effects of a number of replacement strategies for the use in steady state genetic algorithms.The last, "conservative" replacement was developed for the use in time-varying problems and combines a Replace-Oldest strategy with modified selection tournaments.
Frank Vavak and Terence C. Fogarty
The objective of this study is a comparison of two models of the genetic algorithm, the generational and incremental/teady state genetic algorithms, for use in nonstationary/dynamic environments.
Enrique Alba and José M. Troya
We present a first attempt in applying a genetic algorithm for checking the correctness of communication protocols (expressed as a pair of communicating FSMs). We have tested this genetic validation on a hand-made protocol and on TCP.
A.J. Keane
This paper briefly reviews the behaviour of four different evolutionary optimization methods when applied to a pair of difficult, high dimension test functions. The methods considered are the GA, EP, ES and SA.
P. Chardaire, A. Kapsalis, J. W. Mann, V. J. Rayward-Smith and G. D. Smith
GA combinatorial optimisation This paper describes some of the genetic algorithm, or , work undertaken by the group at the Univer- sity of East Anglia on a range of telecommunications applications.
J.E. Smith and T.C.Fogarty
A number of authors have proposed methods of adaptively controlling one or more of the operators, in order to try and improve the performance of the Genetic Algorithm as a function optimiser. In this paper we describe the background to these approaches.
Sami Khuri, W. Melody Moh, Flora Chung
CDMA is an important spread spectrum communication technology for wireless and mobile networks. We investigate the code assignment problem on a CDMA network with hidden terminal interference, which has been proven to be NP-hard.
P. Larrañaga, B. Sierra, M. Y. Gallego, M. J. Michelena and J. M. Pikaza
In this work we introduce a methodology based on Genetic Algorithms for the automatic induction of Bayesian Networks from a file containing cases and variables related to the problem.
Ellie Baker and Margo Seltzer
This paper explores the application of interactive genetic algorithms to the creation of line drawings. We have built a system that starts with a collection of drawings that are either randomly generated or input by the user.
Masaharu Munetomo and David E. Goldberg
The resulting LINC-GA performs genetic algorithms inside the linkage groups obtained by the LINC procedure to find candidates of building blocks and then mixes them to obtain optimal or suboptimal solutions.