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159
A Comparison of Genetic Sequencing Operators
 Proceedings of the fourth International Conference on Genetic Algorithms
, 1991
"... This work compares six sequencing operators that have been developed for use with genetic algorithms. An improved version of the edge recombination operator is presented, the concepts of adjacency, order, and position are reviewed in the context of these operators, and results are compared for ..."
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Cited by 88 (4 self)
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This work compares six sequencing operators that have been developed for use with genetic algorithms. An improved version of the edge recombination operator is presented, the concepts of adjacency, order, and position are reviewed in the context of these operators, and results are compared for a 30 city "Blind" Traveling Salesman Problem and a real world warehouse/shipping scheduling application.
VLSI cell placement techniques
 ACM Computing Surveys
, 1991
"... VLSI cell placement problem is known to be NP complete. A wide repertoire of heuristic algorithms exists in the literature for efficiently arranging the logic cells on a VLSI chip. The objective of this paper is to present a comprehensive survey of the various cell placement techniques, with emphasi ..."
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Cited by 76 (0 self)
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VLSI cell placement problem is known to be NP complete. A wide repertoire of heuristic algorithms exists in the literature for efficiently arranging the logic cells on a VLSI chip. The objective of this paper is to present a comprehensive survey of the various cell placement techniques, with emphasis on standard ce11and macro
Genetic Set Recombination and its Application to Neural Network Topology Optimisation
 NEURAL COMPUTING AND APPLICATIONS
, 1993
"... Forma analysis is applied to the task of optimising the connectivity of a feedforward neural network with a single layer of hidden units. This problem is reformulated as a multiset optimisation problem and techniques are developed to allow principled genetic search over fixed and variablesi ..."
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Cited by 65 (3 self)
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Forma analysis is applied to the task of optimising the connectivity of a feedforward neural network with a single layer of hidden units. This problem is reformulated as a multiset optimisation problem and techniques are developed to allow principled genetic search over fixed and variablesize sets and multisets. These techniques require a further generalisation of the notion of gene, which is presented. The result is a nonredundant representation of the neural network topology optimisation problem together with recombination operators which have carefully designed and wellunderstood properties. The techniques developed have relevance to the application of genetic algorithms to constrained optimisation problems.
Fitness Variance of Formae and Performance Prediction
, 1994
"... Representation is widely recognised as a key determinant of performance in evolutionary computation. The development of families of representationindependentoperators allows the formulation of formal representationindependent evolutionary algorithms. These formal algorithms can be instantiated ..."
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Cited by 61 (7 self)
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Representation is widely recognised as a key determinant of performance in evolutionary computation. The development of families of representationindependentoperators allows the formulation of formal representationindependent evolutionary algorithms. These formal algorithms can be instantiated for particular search problems by selecting a suitable representation. The performance of different representations, in the context of any given formal representationindependent algorithm, can then be measured. Simple analyses suggest that fitness variance of formae (generalised schemata) for the chosen representation might act as a performance predictor for evolutionary algorithms. This hypothesis is tested and supported through studies of four different representations for the travelling salesrep problem (TSP) in the context of both formal representationindependentgenetic algorithms and corresponding memetic algorithms. 1 Motivation The subject of this paper is representation i...
Learning Bayesian Network Structures by Searching For the Best Ordering With Genetic Algorithms
 IEEE Transactions on Systems, Man and Cybernetics
, 1996
"... In this paper we present a ne_(l n [!ii ' with respect to Bayesian networks con ogy for inducing Bayesian network structures frop3 titute the roblem of the evidence propagation and a database of cases. The methodology is based oap&lll searching for the best ordering of the system vari th ..."
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Cited by 55 (9 self)
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In this paper we present a ne_(l n [!ii ' with respect to Bayesian networks con ogy for inducing Bayesian network structures frop3 titute the roblem of the evidence propagation and a database of cases. The methodology is based oap&lll searching for the best ordering of the system vari the problem of the model search. The problem of shies by means of genetic algorithl{. Since his th_vidence propagation consists of once the vMproblem of finding an optimal ordea. teeuarue}rables are known, the assignment of resembles the traveling salesman p'FolUleh)ve use .... IW. ....... probablhles to the values of the rest of the van genetic operators that were developed for the latter  problem. The quality of a variable ordering is eval ables. Cooper [4] demonstrated that this problem Mated with the algorithm K2. We present empirical results that were obtained with a simulation of the ALARM network.
SEARCH, polynomial complexity, and the fast messy genetic algorithm
, 1995
"... Blackbox optimizationoptimization in presence of limited knowledge about the objective functionhas recently enjoyed a large increase in interest because of the demand from the practitioners. This has triggered a race for new high performance algorithms for solving large, difficult problems. Si ..."
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Cited by 51 (10 self)
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Blackbox optimizationoptimization in presence of limited knowledge about the objective functionhas recently enjoyed a large increase in interest because of the demand from the practitioners. This has triggered a race for new high performance algorithms for solving large, difficult problems. Simulated annealing, genetic algorithms, tabu search are some examples. Unfortunately, each of these algorithms is creating a separate field in itself and their use in practice is often guided by personal discretion rather than scientific reasons. The primary reason behind this confusing situation is the lack of any comprehensive understanding about blackbox search. This dissertation takes a step toward clearing some of the confusion. The main objectives of this dissertation are: 1. present SEARCH (Search Envisioned As Relation & Class Hierarchizing)an alternate perspective of blackbox optimization and its quantitative analysis that lays the foundation essential for transcending the limits of random enumerative search; 2. design and testing of the fast messy genetic algorithm. SEARCH is a general framework for understanding blackbox optimization in terms of relations,
Designer Genetic Algorithms: Genetic Algorithms in Structure Design
 Proceedings of the Fourth International Conference on Genetic Algorithms
, 1991
"... This paper considers the problem of using genetic algorithms to design structures. We relax one constraint on classical genetic algorithms and describe a genetic algorithm that uses differential information about search direction to design structures. This differential information is captured by a m ..."
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Cited by 38 (7 self)
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This paper considers the problem of using genetic algorithms to design structures. We relax one constraint on classical genetic algorithms and describe a genetic algorithm that uses differential information about search direction to design structures. This differential information is captured by a masked crossover operator which also removes the bias toward short schemas. We analyze performance and present some preliminary results. Further, consideration of this problem suggests a partial solution to the identification of the deception problem. 1 INTRODUCTION The problem of designing structures is pervasive in science and engineering. The problem is: Given a function and some materials to work with, design a structure that performs this function subject to certain constraints. As an example of the design problem, consider the combinational circuit design problem: Given a set of logic gates, design a circuit that performs a desired function. Two instantiations of this problem are the...
Composing with Genetic Algorithms
 International Computer Music Association
, 1995
"... Presented is an application of genetic algorithms to the problem of composing music, in which GAs are used to produce a set of data filters that identify acceptable material from the output of a stochastic music generator. The algorithmic composition system variations is described and musical exampl ..."
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Cited by 38 (1 self)
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Presented is an application of genetic algorithms to the problem of composing music, in which GAs are used to produce a set of data filters that identify acceptable material from the output of a stochastic music generator. The algorithmic composition system variations is described and musical examples of its output are given. Also discussed briefly is the system's application to microtonal music.
Genetic Algorithms, Path Relinking and the Flowshop Sequencing Problem
 Evolutionary Computation
, 1998
"... In a previous paper (Reeves, 1995), a simple genetic algorithm (GA) was developed for finding (approximately) the minimum makespan of the njob, mmachine permutation flowshop sequencing problem (PFSP). The performance of the algorithm was comparable to that of a naive neighbourhood search technique ..."
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Cited by 35 (1 self)
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In a previous paper (Reeves, 1995), a simple genetic algorithm (GA) was developed for finding (approximately) the minimum makespan of the njob, mmachine permutation flowshop sequencing problem (PFSP). The performance of the algorithm was comparable to that of a naive neighbourhood search technique and a proven Simulated Annealing algorithm. However, recent results (Nowicki & Smutnicki, 1996) have demonstrated the superiority of a tabu search method in solving the PFSP. In this paper, we reconsider the implementation of a GA for this problem, and show that by taking into account the features of the landscape generated by the operators used, we are able to improve its performance significantly. 1 Introduction Finding optimal solutions to large combinatorial problems (COPs) is not in general a realistic endeavour, as has been recognized ever since the implications of the concept of computational complexity (Garey & Johnson, 1979) have been realized. One effect of this recognition has ...
Decomposing Bayesian Networks: Triangulation of Moral Graph with Genetic Algorithms
 Statistics and Computing
, 1997
"... In this paper we consider the optimal decomposition of Bayesian networks. More concretely, we examine  empirically , the applicability of genetic algorithms to the problem of the triangulation of moral graphs. This problem constitutes the only difficult step in the evidence propagation algorithm ..."
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Cited by 22 (4 self)
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In this paper we consider the optimal decomposition of Bayesian networks. More concretely, we examine  empirically , the applicability of genetic algorithms to the problem of the triangulation of moral graphs. This problem constitutes the only difficult step in the evidence propagation algorithm of Lauritzen and Spiegelhalter (1988) and is known to be NPhard (Wen, 1991). We carry out experiments with distinct crossover and mutation operators and with different population sizes, mutation rates and selection biasses. The results are analyzed statistically. They turn out to improve the results obtained with most other known triangulation methods (Kjaerulff, 1990) and are comparable to the ones obtained with simulated annealing (Kjaerulff, 1990; Kjaerulff, 1992). Keywords: Bayesian networks, genetic algorithms, optimal decomposition, graph triangulation, moral graph, NPhard problems, statistical analysis. 1 Introduction The Bayesian networks constitute a reasoning method based on p...