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...

Due to an increasing interest in solving real-world optimization problems using evolutionary algorithms (EAs), researchers have developed a number of real-parameter genetic algorithms (GAs) in the recent past. In such studies, the main research effort is spent on developing an efficient recombination operator. Such recombination operators use probability distributions around the parent solutions to create an ospring. Some operators emphasize solutions at the center of mass of parents and some around the parents. In this paper, we propose a generic parent-centric recombination operator (PCX) and a steady-state, elite-preserving, scalable, and computationally fast population-alteration model (we called the G3 model). The performance of the G3 model with the PCX operator is investigated on three commonly-used test problems and is compared with a number of evolutionary and classical optimization algorithms including other real-parameter GAs with UNDX and SPX operators, the correlated self-adaptive evolution strategy, the dierential evolution technique and the quasi-Newton method. The proposed approach is found to be consistently and reliably performing better than all other methods used in the study. A scale-up study with problem sizes up to 500 variables shows a polynomial computational complexity of the proposed approach. This extensive study clearly demonstrates the power of the proposed technique in tackling real-parameter optimization problems.

Fuzzy Rule-Based Systems have been succesfully applied to pattern classification problems. In this type of classification systems, the classical Fuzzy Reasoning Method (FRM) classifies a new example with the consequent of the rule with the greatest degree of association. By using this reasoning method, we lose the information provided by the other rules with different linguistic labels which also represent this value in the pattern attribute, although probably to a lesser degree. The aim of this paper is to present new FRMs which allow us to improve the system performance, maintaining its interpretability. The common aspect of the proposals is the participation, in the classification of the new pattern, of the rules that have been fired by such pattern. We formally describe the behaviour of a general reasoning method, analyze six proposals for this general model, and present a method to learn the parameters of these FRMs by means of Genetic Algorithms, adapting the inference mechanism ...

The purpose of this paper is to present a genetic learning process for learning fuzzy control rules from examples. It is developed in three stages: the first one is a fuzzy rule genetic generating process based on a rule learning iterative approach, the second one combines two kinds of rules, experts rules if there are and the previously generated fuzzy control rules, removing the redundant fuzzy rules, and the third one is a tuning process for adjusting the membership functions of the fuzzy rules. The three components of the learning process are developed formulating suitable Genetic Algorithms. Keywords: Fuzzy logic control systems, learning, genetic algorithms. 1 Introduction Fuzzy rule based systems have been shown to be an important tool for modelling complex systems, in which due to the complexity or the imprecision, classical tools are unsuccessful. Fuzzy Logic Controllers (FLCs) are now considered as one of the most important applications of the fuzzy rule based systems. The e...

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 ...

Genetic algorithms are adaptive methods which may be used to solve search and optimization problems. Genetic algorithms process a population of search space solutions with three operations: selection, crossover and mutation. An important problem in the use of genetic algorithms is the premature convergence in a local optimum. Their main causes are the lack of diversity in the population and the disproportionate relationship between exploitation and exploration. The crossover operator is considered one of the most determinant elements for solving this problem. In this paper, we present new crossover operators based on fuzzy connectives for real-coded genetic algorithms. These operators are designed to avoid the premature convergence problem. To do so, they should keep the right exploitation/exploration balance to suitably model the diversity of the population. Keywords: Genetic Algorithms, Premature Convergence, Fuzzy Connectives. 1 Introduction Genetic algorithms (GA) are search algo...

The use of Genet ic Algorit hms (GAs) and ot her evolut ionary opt imizat ion met hodst o design fuzzy rules forsyst4E modeling anddat classificat73 have received much at4L t ion in recent litn at ure.AutL rs have focused on various aspect oft hese randomizedtz hniques, and a whole scale of algoritW0 have been proposed. We comment on some recent work and describe a new and e#cient t wo-st5 approacht hat leads t good result forfunct3 n approximat ion, dynamic systNE modeling and da t classificat ion problems. First fuzzyclust5 ing is appliedt o obt in a compact initL7 rule-based model. Then ten model is optB6B3W by a real-coded GA subject4 t const raint st hat maint aint he semant ic propert ies oft he rules. We consider four examples from to litE657W0N a syntW386 nonlinear dynamic systcW model,t he Iris dat classificatNE problem, to Wine dat a classificat ion problem andt he dynamic modeling of a Diesel engine tW bocharger. The obt3845 result are comparedt o otB5 recentc proposed met8 ...

. The genetic algorithm behaviour is determined by the exploitation and exploration relationship kept throughout the run. Adaptive genetic algorithms have been built for inducing exploitation/exploration relationships that avoid the premature convergence problem and improve the final results. One of the most widely studied adaptive approaches are the adaptive parameter setting techniques. In this paper, we study these techniques in depth, based on the use of fuzzy logic controllers. Furthermore, we design and discuss an adaptive realcoded genetic algorithm based on the use of fuzzy logic controllers. Although suitable results have been obtained by using this type of adaptive technique, we report some reflections on open problems that still remain. Keywords. Exploitation/exploration relationship, adaptive genetic algorithms, fuzzy logic controllers. 1 Introduction GA behaviour is strongly determined by the balance between exploiting what already works best and exploring possibilities t...

In this paper, we present a multistage genetic learning process for obtaining linguistic fuzzy rule-based classification systems that integrates fuzzy reasoning methods cooperating with the fuzzy rule base and learns the best set of linguistic hedges for the linguistic variable terms. We show the application of the genetic learning process to two well known sample bases, and compare the results with those obtained from different learning algorithms. The results show the good behavior of the proposed method, which maintains the linguistic description of the fuzzy rules. � 1998 John Wiley & Sons, Inc. 1.

In this paper, a new multiobjective evolutionary algorithm for Environmental/Economic power Dispatch (EED) optimization problem is presented. The EED problem is formulated as a nonlInear constrained multiobjective optimization problem with both equality and inequality constraints. A new Nondominated Sorting Genetic Algorithm (NSGA) based approach is proposed to handle the problem as a true multiobjective optimization problem with competing and non-commensurable objectives. The proposed approach employs a diversity-preserving technique to overcome the premature convergence and search bias problems and produce a well-distributed Pareto-optimal set of nondominated solutions. A hierarchical clustering technique is also imposed to provide the decision maker with a representative and manageable Pareto- optimal set. Several optimization runs of the proposed approach are carded out on a standard IEEE test system. The results demonstrate the capabilities of the proposed NSGA based approach to generate the true Pareto-optimal set of nondominated solutions of the multiobjective EED problem in one single run. Simulation results with the proposed approach have been compared to those reported in the literature. The comparison shows the superiority of the proposed NSGA based approach and confirms its potential to solve the multiobjective EED problem.