s: Jan. 1992 -- Dec. 1994 ffl CTI: Current Technology Index Jan./Feb. 1993 -- Jan./Feb. 1994 ffl DAI: Dissertation Abstracts International: Vol. 53 No. 1 -- Vol. 55 No. 4 (1994) ffl EEA: Electrical & Electronics Abstracts: Jan. 1991 -- Dec. 1994 ffl P: Index to Scientific & Technical Proceedings: Jan. 1986 -- Feb. 1995 (except Nov. 1994) ffl EI A: The Engineering Index Annual: 1987 -- 1992 ffl EI M: The Engineering Index Monthly: Jan. 1993 -- Dec. 1994 The following GA researchers have already kindly supplied their complete autobibliographies and/or proofread references to their papers: Dan Adler, Patrick Argos, Jarmo T. Alander, James E. Baker, Wolfgang Banzhaf, Ralf Bruns, I. L. Bukatova, Thomas Back, Yuval Davidor, Dipankar Dasgupta, Marco Dorigo, Bogdan Filipic, Terence C. Fogarty, David B. Fogel, Toshio Fukuda, Hugo de Garis, Robert C. Glen, David E. Goldberg, Martina Gorges-Schleuter, Jeffrey Horn, Aristides T. Hatjimihail, Mark J. Jakiela, Richard S. Judson, Akihiko Konaga...

It is a common engineering practice to use approximate models instead of the original computationally expensive model in optimization. When an approximate model is used for evolutionary optimization, the convergence properties of the evolutionary algorithm are unclear due to the approximation error. In this paper, extensive empirical studies on convergence of an evolution strategy are carried out on two bench-mark problems. It is found that incorrect convergence will occur if the approximate model has false optima. To address this problem, individual and generation based evolution control is introduced and the resulting effects on the convergence properties are presented. A framework for managing approximate models in generation-based evolution control is proposed. This framework is well suited for parallel evolutionary optimization that is able to guarantee the correct convergence of the evolutionary algorithm and to reduce the computation costs as much as possible. Control o...

Genetic and evolutionary algorithms have been increasingly applied to solve complex, large scale search problems with mixed success. Competent genetic algorithms have been proposed to solve hard problems quickly, reliably and accurately. They have rendered problems that were difficult to solve by the earlier GAs to be solvable, requiring only a subquadratic number of function evaluations. To facilitate solving large-scale complex problems, and to further enhance the performance of competent GAs, various efficiency-enhancement techniques have been developed. This study investigates one such class of efficiency-enhancement technique called evaluation relaxation. Evaluation-relaxation schemes replace a high-cost, low-error fitness function with a low-cost, high-error fitness function. The error in fitness functions comes in two flavors: Bias and variance. The presence of bias and variance in fitness functions is considered in isolation and strategies for increasing efficiency in both cases are developed. Specifically, approaches for choosing between two fitness functions with either differing variance or differing bias values have been developed. This thesis also investigates fitness inheritance as an evaluation-

Genetic algorithm behavior is determined by the exploration/exploitation balance kept throughout the run. When this balance is disproportionate, the premature convergence problem will probably appear, causing a drop in the genetic algorithm's efficacy. One approach presented for dealing with this problem is the distributed genetic algorithm model. Its basic idea is to keep, in parallel, several subpopulations that are processed by genetic algorithms, with each one being independent from the others. Furthermore, a migration mechanism produces a chromosome exchange between the subpopulations. Making distinctions between the subpopulations by applying genetic algorithms with different configurations, we obtain the so-called heterogeneous distributed genetic algorithms. These algorithms represent a promising way for introducing a correct exploration/exploitation balance in order to avoid the premature convergence problem and reach approximate final solutions. In this paper, we present the ...

This paper presents models that predict the speedup of two cases that bound the possible topologies and migration rates of parallel genetic algorithms (GAs). The first bounding case is a parallel GA with completely isolated demes or subpopulations and for this case the model and the experiments show that the speedup is not very significant when more demes are used. The second model predicts the speedup when each deme communicates with every other deme using a maximal migration rate. For this case, we show that when the communication time is not constant there is a combination of number of demes and deme size that maximizes the speedup. The models are validated with computational experiments using functions of varying difficulty. 1 Introduction Many claims have been made about the practical benefits of parallel genetic algorithms. To quantify these benefits we can compute the expected parallel speedup, comparing a parallel GA with a simple GA that finds a solution of the same quality....

This paper presents models to predict the quality of convergence of idealized bounding cases of parallel genetic algorithms (GAs). The first bounding case is a parallel GA with completely isolated subpopulations (demes). We show how the probability that the parallel GA finds a solution of the minimum desired quality increases as more demes are used. Our second bounding case considers that each deme communicates with every other deme with a maximal migration rate. Our models predict the probability that a locus converges to the correct value and are based on a previous model for simple GAs. For each of the bounding cases, we derive equations to determine the deme size that is required when the quality of the solution and the number of demes are fixed. 1 Introduction The practical benefits of parallel genetic algorithms (GAs) have been recognized for a long time, but their design has many difficult and interrelated problems. The three main problems are to determine (1) the size and the ...

Genetic algorithms (GAs) are search methods based on principles of natural selection and genetics (Fraser, 1957; Bremermann, 1958; Holland, 1975). We start with a brief introduction to simple genetic algorithms and associated terminology. GAs encode

The paper presents the design of a hardware genetic algorithm which uses a pipeline of systolic arrays. Demostrated is the design methodology, where a simple genetic algorithm expressed in C source code is progressivly re-written into a recurrence form from which systolic structures can be deduced. The paper extends previous work by the authors by introducing a simplification to a previous systolic design. 1 Introduction The Genetic Algorithm (GA) is now a well established technique for search and optimization [3]. Inherent to the technique is a rich source of parallelism, which exists at many levels, and this has attracted the attention of the parallel research community. By using parallelism it is possible to speed up the execution of the algorithm. Thus the technique can be applied to larger problems or deliver acceptable performance in real-time applications. Attempts to this end have been made on both generalpurpose parallel architectures and, more recently, on special purpose ...

A parallel implementation of Genetic Programming is described, using the Bulk Synchronous Parallel Programming (BSP) model, as implemented by the Oxford BSP library. It is shown that considerable speedup of the GP execution can be achieved. As the complexity and the size of the problem increases, the actual speedup can be improved (assuming a constant number of processors), since the communication overhead is small compared with the parallel GP parts. The ease of use of BSP and the speedup achieved with the corresponding parallel implementation, suggests that GP researchers should consider BSP parallel implementations when dealing with time consuming problems.

A cellular automaton is a decentralized computing model providing an excellent platform for performing complex computation with the help of only local information. Researchers, scientists and practitioners from different fields have exploited the CA paradigm of local information, decentralized control and universal computation for modeling different applications. This article provides a survey of available literature of some of the methodologies employed by researchers to utilize cellular automata for modeling purposes. The survey introduces the different types of cellular automata being used for modeling and the analytical methods used to predict its global behavior from its local configurations. It further gives a detailed sketch of the efforts undertaken to configure the local settings of CA from a given global situation; the problem which has been traditionally termed as the inverse problem. Finally, it presents the different fields in which CA have been applied. The extensive bibliography provided with the article will be of help to the new entrant as well as researchers working in this field.