The main goal of this paper is to summarize the previous research on parallel genetic algorithms. We present an extension to previous categorizations of the parallelization techniques used in this field. We will use this categorization to guide us through a review of many of the most important publications. We will build on this survey to try to identify some of the problems that have not been studied systematically yet. 1 Introduction Genetic Algorithms (GAs) are efficient search methods based on principles of natural selection and population genetics. They are being successfully applied to problems in business, engineering and science (Goldberg, 1994). GAs use randomized operators operating over a population of candidate solutions to generate new points in the search space. In the past few years, parallel genetic algorithms (PGAs) have been used to solve difficult problems. Hard problems need a bigger population and this translates directly into higher computational costs. The basic...

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 ...

In this paper we address an extension of a very efficient genetic algorithm (GA) known as Hy3, a physical parallelization of the gradual distributed real-coded GA (GD-RCGA). This search model relies on a set of eight subpopulations residing in a cube topology having two faces for promoting exploration and exploitation. The resulting technique has been shown to yield very accurate results in continuous optimization by using crossover operators tuned to explore and exploit the solutions inside each subpopulation. We introduce here a further extension of Hy3, called Hy4, that uses 16 islands arranged in a hypercube of four dimensions. Thus, two new faces with different exploration/exploitation search capabilities are added to the search performed by Hy3. We analyze the importance of running a synchronous versus an asynchronous version of the models considered. The results indicate that the proposed Hy4 model overcomes the Hy3 performance because of its improved balance between exploration and exploitation that enhances the search. Finally, we also show that the async Hy4 model scales better than the sync one.

We have designed a highly parallel design for a simple genetic algorithm using a pipeline of systolic arrays. The systolic design provides high throughput and unidirectional pipelining by exploiting the implicit parallelism in the genetic operators. The design is significant because, unlike other hardware genetic algorithms, it is independent of both the fitness function and the particular chromosome length used in a problem. We have designed and simulated a version of the mutation array using Xilinix FPGA tools to investigate the feasibility of hardware implementation. A simple 5-chromosome mutation array occupies 195 CLBs and is capable of performing more than one million mutations per second. I. Introduction Genetic algorithms (GAs) are established search and optimization techniques which have been applied to a range of engineering and applied problems with considerable success [1]. They operate by maintaining a population of trial solutions encoded, using a suitable encoding schem...

This paper analyzes the detection of stagnation states in iterated local search algorithms. This is done considering elements such as the population size, the length of the encoding and the number of observed non-improving iterations. This analysis isolates the features of the target problem within one parameter for which three di#erent estimations are given: two static a priori estimations and a dynamic approach. In the latter case, a stochastic reverse hillclimbing algorithm is used to extract information from the fitness landscape. The applicability of these estimations is studied and exemplified on di#erent problems.

This study first introduces an order-free chromosome encoding to enhance the performance of genetic algorithms by learning the linkage of building blocks in non-binary encodings. The method introduces a measure called affinity which is based on the statistical properties of gene valuations in the population. It uses the affinity values of the local and global gene pairs to construct a global permutation with tight building block positioning. Method is tested and experimental results are shown for a group of deceptive and real life test problems.

udy Institute Course, Spain, 1996. ffl Conference travel grant from Sigma-Xi, UIUC for the paper titled, Drift, diffusion, and Boltzmann distribution in simple genetic algorithm (Kargupta, 1992). ffl Conference travel grant from Sigma-Xi, UIUC for the paper titled, Temporal sequence processing based on the biological reaction-diffusion process (Kargupta & Ray, 1994). ffl Honors Distinction in B. Tech., 1988. ffl National Talent Certificate (India), 1982. PROPOSALS/FUNDING Title: Classification and Visualization of Textual Data Principal investigator: Hillol Kargupta Status: Awarded 35K (July, 1996) Sponsoring agency: Caterpillar and National Center for Supercomputing Applications Title: Market Scoping Activity for PADMA, An Agent Based System For Text Classification Principal investigators: Hillol Kargupta and Dave Forslund (Advanced Computing Laboratory,

Genetic Algorithms (GAs) are commonly used search algorithms and there is an incentive in accelerate their execution speed using hardware. We present a collection of systolic array designs which perform the Selection, Crossover and Mutation operations of the GA. Although the premise there is considerable generality in the genetic operators is true, it is accepted that GAs often use different techniques depending on the problem being solved. We have therefore included additional arrays which can be implemented to perform alternative or complementary operations. The dataflow through the arrays allows different arrays to be implemented independently of others in the configuration. The intention is to form a library of operators which can be used to implement a tailored GA, based on a plug and play approach. We propose a parallel processing model to this end which uses a general purpose processor, coupled with a Field Programmable Gate Array. This allows a mixed hardware/software approach ...