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20
Tackling RealCoded Genetic Algorithms: Operators and Tools for Behavioural Analysis
 Artificial Intelligence Review
, 1998
"... . Genetic algorithms play a significant role, as search techniques for handling complex spaces, in many fields such as artificial intelligence, engineering, robotic, etc. Genetic algorithms are based on the underlying genetic process in biological organisms and on the natural evolution principles of ..."
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Cited by 123 (24 self)
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. Genetic algorithms play a significant role, as search techniques for handling complex spaces, in many fields such as artificial intelligence, engineering, robotic, etc. Genetic algorithms are based on the underlying genetic process in biological organisms and on the natural evolution principles of populations. These algorithms process a population of chromosomes, which represent search space solutions, with three operations: selection, crossover and mutation. Under its initial formulation, the search space solutions are coded using the binary alphabet. However, the good properties related with these algorithms do not stem from the use of this alphabet; other coding types have been considered for the representation issue, such as real coding, which would seem particularly natural when tackling optimization problems of parameters with variables in continuous domains. In this paper we review the features of realcoded genetic algorithms. Different models of genetic operators and some me...
A hierarchy of evolution programs: An experimental study
 Evolutionary Computation
, 1993
"... zbyszek�mosaic.uncc.edu In this paper we present the concept of evolution programs and discuss a hier� archy of such programs for a particular problem. We argue that �for a particular problem � stronger evolution programs �in terms of the problem�speci�c knowledge incorporated in the system � should ..."
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Cited by 19 (4 self)
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zbyszek�mosaic.uncc.edu In this paper we present the concept of evolution programs and discuss a hier� archy of such programs for a particular problem. We argue that �for a particular problem � stronger evolution programs �in terms of the problem�speci�c knowledge incorporated in the system � should perform better than weaker ones. This hypothe� sis is based on a number of experiments and a simple intuition that problem�speci�c knowledge enhances an algorithm in terms of its performance � at the same time it narrows the applicability of an algorithm. Trade�o�s between the e�ort of �nding an e�ective representation for general�purpose evolution programs and the e�ort of developing more specialized systems are also discussed. 1
Design of adaptive fuzzy logic controller based on linguistichedge concepts and genetic algorithms
 IEEE Transactions on Systems, Man, and Cybernetics – Part B: Cybernetics
, 2001
"... Abstract—In this paper, we propose a novel fuzzy logic controller, called linguistic hedge fuzzy logic controller, to simplify the membership function constructions and the rule developments. The design methodology of linguistic hedge fuzzy logic controller is a hybrid model based on the concepts of ..."
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Cited by 10 (0 self)
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Abstract—In this paper, we propose a novel fuzzy logic controller, called linguistic hedge fuzzy logic controller, to simplify the membership function constructions and the rule developments. The design methodology of linguistic hedge fuzzy logic controller is a hybrid model based on the concepts of the linguistic hedges and the genetic algorithms. The linguistic hedge operators are used to adjust the shape of the system membership functions dynamically, and can speed up the control result to fit the system demand. The genetic algorithms are adopted to search the optimal linguistic hedge combination in the linguistic hedge module. According to the proposed methodology, the linguistic hedge fuzzy logic controller has the following advantages: 1) it needs only the simpleshape membership functions rather than the carefully designed ones for characterizing the related variables; 2) it is sufficient to adopt a fewer number of rules for inference; 3) the rules are developed intuitionally without heavily depending on the endeavor of experts; 4) the linguistic hedge module associated with the genetic algorithm enables it to be adaptive; 5) it performs better than the conventional fuzzy logic controllers do; and 6) it can be realized with low design complexity and small hardware overhead. Furthermore, the proposed approach has been applied to design three wellknown nonlinear systems. The simulation and experimental results demonstrate the effectiveness of this design. Index Terms—Adaptive fuzzy logic controller, genetic algorithm, linguistic hedge. I.
Designing genetic algorithms for the state assignment problem
 IEEE Transactions on Systems, Man, and Cybernetics
, 1995
"... Abstract — Finding the best state assignment for implementing a synchronous sequential circuit is important for reducing silicon area or chip count in many digital designs. This State Assignment Problem (SAP) belongs to a broader class of combinatorial optimization problems than the well studied tra ..."
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Cited by 8 (0 self)
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Abstract — Finding the best state assignment for implementing a synchronous sequential circuit is important for reducing silicon area or chip count in many digital designs. This State Assignment Problem (SAP) belongs to a broader class of combinatorial optimization problems than the well studied traveling salesman problem, which can be formulated as a special case of SAP. The search for a good solution is considerably involved for the SAP due to a large number of equivalent solutions, and no effective heuristic has been found so far to cater to all types of circuits. In this paper, a matrix representation is used as the genotype for a Genetic Algorithm (GA) approach to this problem. A novel selection mechanism is introduced, and suitable genetic operators for crossover and mutation, are constructed. The properties of each of these elements of the GA are discussed and an analysis of parameters that influence the algorithm is given. A canonical form for a solution is defined to significantly reduce the search space and number of local minima. Experiments with several examples show that the GA approach yields results that are often comparable to, or better than those obtained using established heuristics that embody extensive domain knowledge.
Evolutionary computation techniques for nonlinear programming problems
 International Transactions of Operational Research
, 1994
"... zbyszek�mosaic.uncc.edu The paper presents several evolutionary computation techniques and discusses their applicability to nonlinear programming problems. On the basis of this presen� tation we discuss also a construction of a new hybrid optimization system � Genocop II � and present its experiment ..."
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Cited by 8 (0 self)
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zbyszek�mosaic.uncc.edu The paper presents several evolutionary computation techniques and discusses their applicability to nonlinear programming problems. On the basis of this presen� tation we discuss also a construction of a new hybrid optimization system � Genocop II � and present its experimental results on a few test cases �nonlinear programming problems�. Keywords � evolutionary computation � genetic algorithm � random algorithm � optimiza� tion technique � nonlinear programming � constrained optimization.
Applying Genetic Algorithms in Fuzzy Optimization Problems
 Fuzzy Systems & Artificial Intelligence Reports and Letters
, 1994
"... Genetic Algorithms as function optimizers are global optimization techniques based on natural selection principles that can be efficiently used in high dimensional, multimodal and complex problems. Genetic Algorithms are here presented as a tool to solve fuzzy optimization problems either on their a ..."
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Cited by 7 (2 self)
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Genetic Algorithms as function optimizers are global optimization techniques based on natural selection principles that can be efficiently used in high dimensional, multimodal and complex problems. Genetic Algorithms are here presented as a tool to solve fuzzy optimization problems either on their associated auxiliary models or by assuming the existence of some fuzzy performance (fitness) function. Finally applications of Genetic Algorithms to find the maximum flow in a network with fuzzy capacities and assignment problems with linguistic labels are studied. Keywords: Genetic algorithms, fuzzy optimization. 1. Introduction Methods and techniques of optimization have been successfully used in various fields, and related to technical systems of relatively welldefined structure and behavior, the socalled hard ones. The success has motivated a direct application of the same traditional approaches to the modeling and analysis of what is often called the soft systems in which a key role ...
A Genetic Algorithm for the Multiobjective Solid Transportation Problem: A Fuzzy Approach
, 1994
"... Genetic Algorithms are adaptative procedures of optimization and search inspired in the mechanisms of natural selection and genetic. Currently, these algorithms are being highly considered above all in those problems with complex solution spaces for those which we do not have good algorithms to solv ..."
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Cited by 4 (2 self)
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Genetic Algorithms are adaptative procedures of optimization and search inspired in the mechanisms of natural selection and genetic. Currently, these algorithms are being highly considered above all in those problems with complex solution spaces for those which we do not have good algorithms to solve them. To address the Transportation Problem, usualy is supposed the decisionmaker knows with certitude each data involved into it, but often he does not it. In this case, fuzzy sets are a very appropiate tool modeling this kind of process. As occurs in the classical case, Transportation problems in a fuzzy environment ([5]) and Fuzzy Linear Programming ([14]) are strongly related between them, but there are some cases for which the existing algorithms do not work well, e.g. the Multiobjective Solid Transportation Problem (MSTP). In this paper we enhance the interface between Genetic Algorithms and Fuzzy Sets ([8]) by proposing a Genetic Algorithm based solution method to the case in which fuzzy goals are assumed in the MSTP. The following work consists of a first approach to the problem whose solutions are found in integers.
Applying Genetic Algorithms to the State Assignment Problem: A case Study
 In SPIE Conf. on Adaptive and Learning Systems, SPIE Proc
, 1992
"... Finding the best state assignment for implementing a synchronous sequential circuit is important for reducing silicon area or chip count in many digital designs. This State Assignment Problem (SAP) belongs to a broader class of combinatorial optimization problems than the well studied traveling sale ..."
Abstract

Cited by 4 (1 self)
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Finding the best state assignment for implementing a synchronous sequential circuit is important for reducing silicon area or chip count in many digital designs. This State Assignment Problem (SAP) belongs to a broader class of combinatorial optimization problems than the well studied traveling salesman problem, which can be formulated as a special case of SAP. The search for a good solution is considerably more involved for the SAP than it is for the traveling salesman problem due to a much larger number of equivalent solutions, and no effective heuristic has been found so far to cater to all types of circuits. In this paper, a matrix representation is used as the genotype for a Genetic Algorithm (GA) approach to this problem. A novel selection mechanism is introduced, and suitable genetic operators for crossover and mutation, are constructed. The properties of each of these elements of the GA are discussed and an analysis of parameters that influence the algorithm is given. A canonic...
A Genetic Algorithm for Constrained Statistical Matching
, 2007
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