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55
An Indexed Bibliography of Genetic Algorithms in Power Engineering
, 1995
"... 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: Ja ..."
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Cited by 73 (8 self)
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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 GorgesSchleuter, Jeffrey Horn, Aristides T. Hatjimihail, Mark J. Jakiela, Richard S. Judson, Akihiko Konaga...
A Framework for Evolutionary Optimization with Approximate Fitness Functions
 IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
, 2002
"... 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. ..."
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Cited by 72 (12 self)
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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 benchmark 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 generationbased 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...
Evaluationrelaxation schemes for genetic and evolutionary algorithms
, 2002
"... 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 th ..."
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Cited by 60 (28 self)
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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 largescale complex problems, and to further enhance the performance of competent GAs, various efficiencyenhancement techniques have been developed. This study investigates one such class of efficiencyenhancement technique called evaluation relaxation. Evaluationrelaxation schemes replace a highcost, lowerror fitness function with a lowcost, higherror 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
Gradual distributed realcoded genetic algorithms
 151 Z. Michalewicz. Genetic Algorithms + Data Structures = Evolution Programs
, 1999
"... Abstract—A major problem in the use of genetic algorithms is premature convergence, a premature stagnation of the search caused by the lack of diversity in the population. One approach for dealing with this problem is the distributed genetic algorithm model. Its basic idea is to keep, in parallel, s ..."
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Cited by 44 (6 self)
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Abstract—A major problem in the use of genetic algorithms is premature convergence, a premature stagnation of the search caused by the lack of diversity in the population. One approach 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 of 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 socalled heterogeneous distributed genetic algorithms. These algorithms represent a promising way for introducing a correct exploration/exploitation balance in order to avoid premature convergence and reach approximate final solutions. This paper presents the gradual distributed realcoded genetic algorithms, a type of heterogeneous distributed realcoded genetic algorithms that apply a different crossover operator to each subpopulation. The importance of this operator on the genetic algorithm’s performance allowed us to differentiate between the subpopulations in this fashion. Using crossover operators presented for realcoded genetic algorithms, we implement three instances of gradual distributed realcoded genetic algorithms. Experimental results show that the proposals consistently outperform sequential realcoded genetic algorithms and homogeneous distributed realcoded genetic algorithms, which are equivalent to them and other mechanisms presented in the literature. These proposals offer two important advantages at the same time: better reliability and accuracy. Index Terms—Crossover operator, distributed genetic algorithms, multiresolution, premature convergence, selective pressure. I.
Predicting Speedups of Idealized Bounding Cases of Parallel Genetic Algorithms
 In Back, T. (Ed.), Proceedings of the Seventh International Conference on Genetic Algorithms (pp. 113121
, 1997
"... 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 ..."
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Cited by 24 (4 self)
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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....
Modeling Idealized Bounding Cases of Parallel Genetic Algorithms
 In
, 1997
"... 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 minimu ..."
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Cited by 22 (7 self)
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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 ...
Data Aggregation and Roadside Unit Placement for a VANET Traffic Information System
 ACM VANET 2008
, 2008
"... In this paper we investigate how a VANETbased traffic information system can overcome the two key problems of strictly limited bandwidth and minimal initial deployment. First, we present a domain specific aggregation scheme in order to minimize the required overall bandwidth. Then we propose a gene ..."
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Cited by 20 (2 self)
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In this paper we investigate how a VANETbased traffic information system can overcome the two key problems of strictly limited bandwidth and minimal initial deployment. First, we present a domain specific aggregation scheme in order to minimize the required overall bandwidth. Then we propose a genetic algorithm which is able to identify good positions for static roadside units in order to cope with the highly partitioned nature of a VANET in an early deployment stage. A tailored toolchain allows to optimize the placement with respect to an applicationcentric objective function, based on travel time savings. By means of simulation we assess the performance of the resulting traffic information system and the optimization strategy.
A Survey on Cellular Automata
, 2003
"... 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 contr ..."
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Cited by 16 (0 self)
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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.
Genetic Algorithms
, 2005
"... 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 ..."
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Cited by 15 (3 self)
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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
On Cooperation between Evolutionary Algorithms and other
 Search Paradigms, Proc. CEC99
, 1999
"... Abstract We present a multiagent based approach for achieving cooperation between search systems employing different search paradigms. The search agents periodically interrupt their search, select interesting information from their states that is transmitted to the other agents, filter the informa ..."
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Cited by 14 (4 self)
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Abstract We present a multiagent based approach for achieving cooperation between search systems employing different search paradigms. The search agents periodically interrupt their search, select interesting information from their states that is transmitted to the other agents, filter the information sent to them with respect to their own demands, integrate the remaining information into their search, and then continue the search. There are different kinds of information to be exchanged and the selection is both success and demanddriven. We demonstrate the usefulness of this approach by coupling a search system based on a Genetic Algorithm and a branchandbound based system for jobshopscheduling. Our experiments show that the cooperation results in finding better solutions within a given time limit and in finding solutions comparable to those generated by the best system working alone in less time. The speedup factors for some examples even exceed the number of agents (computers) used. 1