G. Dozier, J. Bowen, and D. Bahler. Solving Small and Large Scale Constraint Satisfaction Problems Using a Heuristic-Based Microgenetic Algorithm. Proceedings of the 1994.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....rate of 0.6 and a mutation rate of 0. 001) Krishnakumar [15] reported faster and better results with his micro GA on two stationary functions and a real world engineering control problem (a wind shear controller task) After him, several other researchers have developed applications of micro GAs [13, 7, 12, 21]. However, to the best of our knowledge, the current paper reports the rst attempt to use a micro GA for multiobjective optimization, although some may argue that the multi membered versions of PAES can be seen as a form of micro GA 1 [14] However, Knowles Corne [14] concluded that the ....

G. Dozier, J. Bowen, and D. Bahler. Solving small and large scale constraint satisfaction problems using a heuristic-based microgenetic algorithm. In Proceedings of the First IEEE Conference on Evolutionary Computation, pages 306-311, 1994.

....with respect to this stronger linear constraint may be sup optimal with respect to the original no node label overlap constraints. In contrast the third and fourth approaches allow the disjunctive node overlapping constraint to be expressed directly. Both approaches use local search methods [1, 2, 7, 6, 27]. A local search method starts with a current value for each variable, and by examining the local neighbourhood tries to move to a point which is closer to the optimum. Constraints are handled as penalties to the optimization function. The search proceeds until a local minimum is found. When the ....

G. Dozier, J. Bowen, and D. Bahler. Solving small and large scale constraint satisfaction problems using a heuristic-based microgenetic algorithm. In Proceedings of the IEEE International Conference on Evolutionary Computation, 1994.

....rate of 0.6 and a mutation rate of 0. 001) Krishnakumar [12] reported faster and better results with his micro GA on two stationary functions and a real world engineering control problem (a wind shear controller task) After him, several other researchers have developed applications of micro GAs [10, 6, 9, 17]. However, to the best of our knowledge, the current paper reports the rst attempt to use a micro GA for multiobjective optimization, although some may argue that the multi membered versions of PAES can be seen as a form of micro GA [11] However, Knowles Corne [11] concluded that the addition ....

G. Dozier, J. Bowen, and D. Bahler. Solving small and large scale constraint satisfaction problems using a heuristic-based microgenetic algorithm. In Proceedings of the First IEEE Conference on Evolutionary Computation, pages 306-311, 1994.

....efficiency. This is the idea behind micro genetic algorithms, the subclass of evolutionary algorithms based on a small population size (usually 20) Micro genetic algorithms can find solutions with fewer iterations than evolutionary algorithms with larger population sizes for some problems [10, 6]. To further improve the search efficiency of micro genetic algorithms in solving CSPs, researchers have tried adding different heuristics in the evolutionary computation. For instance, Rojas [13] used a heuristic to define the importance of a constraint in a constraint network on which the ....

....heuristic to define the importance of a constraint in a constraint network on which the fitness function is based. With this more detailed fitness function she was able to improve an evolutionary algorithm in solving a set of randomly generated 3 colouring graphs. On the other hand, Dozier et al. [6] proposed an interesting heuristic inheritance mechanism for their micro genetic algorithm for tackling binary CSPs. The mechanism tries to minimize the number of constraint violations by continuously mutating only a single selected variable, or moving to mutate another variable. Further, they ....

[Article contains additional citation context not shown here]

G. Dozier, J. Bowen, and D. Bahler. Solving small and large scale constraint satisfaction problems using a heuristic-based microgenetic algorithm. In Proceedings of the IEEE International Conference on Evolutionary Computation, pages 306--311, 1994.

.... di#erence in the time required to solve a CSP, 11] Evolutionary methods are based on the evolution theory and they are in the category of stochastic search methods for optimization problems [10] It has been applied to solving Constraint Satisfaction Optimization Problems [20] and CSP [2, 3, 14, 15, 16]. These approaches have concentrated on studying the genetic representation and the reproduction mechanisms. Most of these techniques use fixed representations, operators and control parameters. Since evolutionary algorithms are based on the idea of evolution, it is more than natural to expect ....

....22.4.2 This function is calculated only considering the constraints involved (related to X j ) and whose variables are instantiated in I p 22.4. 2 Constraint Dynamic Adaptive Operator Until now the best results found for the CSP resolution using evolutionary algorithms work with asexual operators [15, 3], rather oriented in the exploration than in the exploitation, owing in part to the epistatic characteristic of CSPs. Furthermore, using a classical crossover operator could produce a performance degradation of the evolutionary algorithm for CSP. Our aim here is to add to the exploitation concept ....

Bowen J., Dozier G. and Bahler D. Solving small and large scale constraint satisfaction problems using a heuristic-based microgenetic algorithm. In Proceedings of the First IEEE Conf on Evolutionary Computation, pages 306--311, 1994.

....is applied to help the network escape from these local minima. The network convergence procedure iterates until a solution is found or a predetermined resource limit is exceeded. Besides ANNs, evolutionary algorithms are another example of local search methods capable of efficiently solving CSPs [5, 2, 11]. To model a CSP in an EA each variable in a CSP is usually represented by a gene, and a valuation for all variables is thus a chromosome. The evaluation function is usually defined in terms of the number of constraint violations. The evolutionary algorithm basically performs a parallel local ....

.... idea behind microgenetic algorithms (MGAs) which is a subclass of evolutionary algorithms based on a small population size (usually 20) Recently, it has been reported that MGAs can find solutions with fewer iterations than evolutionary algorithms with larger population sizes for some problems [8, 5]. Even integrated with some useful heuristics, most MGAs, like the conventional EAs, still depend heavily on the selection criteria of the evaluation function to guide the parallel local searches towards the global optimum of the evaluation function. On solving some moderately or highly ....

[Article contains additional citation context not shown here]

G. Dozier, J. Bowen, and D. Bahler. Solving small and large scale constraint satisfaction problems using a heuristic-based microgenetic algorithm. In Proceedings of the IEEE International Conference on Evolutionary Computation, 1994.

....are many approaches to solve finite CSPs. Enumerative search methods such as chronological backtracking[12] can be slow on solving many real life large scale or difficult CSPs. On the other hand, stochastic search methods such as artificial neural networks (ANNs) 1, 2, 3] evolutionary algorithms[4] and simulated annealing[6] can be more efficient in solving certain real life examples[3, 15] of finite CSPs. Among the stochastic search methods, GENET[14] and its extended model EGENET[7] are the min conflict heuristic (MCH) 9] based ANNs which can solve some difficult finite CSPs such as a set ....

G. Dozier, J. Bowen, and D. Bahler. Solving small and large scale constraint satisfaction problems using a heuristicbased microgenetic algorithm. In Proceedings of the IEEE International Conference on Evolutionary Computation, 1994.

.... constrained optimisation problems [31, 37, 40] Recently GAs have also been applied to the subset sum problem and the minimum tardy task [27] scheduling [41] graph partitioning [2, 28] set covering [25] satisfiability problems [21] timetable problem [36] as well as the N queens problem [6, 10]. In [32] systems for constrained optimization problems with continous domains are discussed. 1.2 Genetic Algorithms Genetic algorithms are generate and test methods with a philosophy and terminology taken from evolution theory [20, 8, 23, 32] They operate on a search space where the quality of ....

Dozier, G., Bowen, J., Bahler, D., Solving small and large scale constraint satisfaction problems using a heuristic-based microgenetic agorithm, In Proc. of the First IEEE Conference on Evolutionary Computation, Orlando, Fl. 1994, pp 306-311.

....than local optimization techniques. It can only recombine good guesses hoping that one recombination will have a better fitness than both of its parents 3 . Because of this limitation, many researchers have combined GAs with other optimization techniques to develop hybrid genetic algorithms [35, 14, 15, 36, 37, 38, 17, 21, 20, 39]. The purpose of such hybrid systems is to speed up the rate of convergence while retaining the ability to avoid being easily entrapped at a local optimum. Although local optimization in a hybrid often results in a faster convergence, it has been shown that too much local optimization can ....

G. Dozier, J. Bowen, and D. Bahler, "Solving small and large scale constraint satisfaction problems using a heuristic-based microgenetic algorithm," In Proceedings of the First IEEE Conference on Evolutionary Computation, Orlando, FL, June 1994.

....than local optimization techniques. It can only recombine good guesses hoping that one recombination will have a better fitness than both of its parents 2 . Because of this limitation, many researchers have combined GAs with other optimization techniques to develop hybrid genetic algorithms [30, 14, 15, 31, 32, 33, 17, 20]. The purpose of such hybrid systems is to speed up the rate of convergence while retaining the ability to avoid being easily entrapped at a local optimum. Although local optimization in a hybrid often results in a faster convergence, it has been shown that too much local optimization can ....

G. Dozier, J. Bowen, and D. Bahler, "Solving small and large scale constraint satisfaction problems using a heuristic-based microgenetic algorithm," In Proceedings of the First IEEE Conference on Evolutionary Computation, Orlando, Florida, June 1994.

....methods are based on the evolution theory and they are in the category of stochastic search methods for optimization problems, 12] The most popular evolutionary method is the Genetic Algorithm. It has been applied to solving Constraint Satisfaction Optimization Problems, 17] and CSP, 2] [5], 6] 7] 8] In these approaches the researchers have concentrated on studying the genetic representation and the reproduction mechanisms. Most of these techniques use xed representations, operators and control parameters. Since evolutionary algorithms implement the idea of evolution, it is ....

....k i ) c(C k j ) P is an ordered j tuple of constraints. Roughly we are ordering the constraints according to their contribution to the tness function Z(I) 4. 2 Self Adap arc Until now the best results found for the CSP resolution using evolutionary algorithms work with asexual operators, 7] [5], more oriented in the exploration than in the exploitation, owing in part to the epistatic characteristic of CSPs (high correlation degree between the genes in the chromosome) Furthermore, using a classical crossover operator could produce a performance degradation of the evolutionary algorithm ....

Dozier G., Bowen J., Bahler D., Solving Small and Large Scale Constraint Satisfaction Problems Using a Heuristic-Based Microgenetic Algorithm. Proc. of the First IEEE Conf on Evolutionary Computation, Orlando, pp 306-311, 1994.

No context found.

G. Dozier, J. Bowen, and D. Bahler. Solving Small and Large Scale Constraint Satisfaction Problems Using a Heuristic-Based Microgenetic Algorithm. Proceedings of the 1994.

....the corporate cultural aspects of concurrent engineering through support for negotiated resolution of design conflicts[1, 3, 4] using notions of economic utility theory. Also, we have now begun to use genetic algorithms as part of our method for both constraint solving and advice generation[5, 6, 7]. This work was partially supported by the National Science Foundation under grant no. DDM 9215755, under ARPA under contract DAAH04 94 G 003 P2, by Dr. Paul Franzon s NSF Young Investigator s Award, by Intel and by IBM Corp. y bahler ncsu.edu, Dept. of Computer Science, Box 8206. z ....

Dozier, G., J. Bowen, and D. Bahler, "Solving Small and Large Scale Constraint Satisfaction Problems Using a Heuristic-Based Microgenetic Algorithm," Proc. IEEE Intl. Conf. on Evolutionary Computation, Orlando, June 1994, 306-311.

Online articles have much greater impact   More about CiteSeer.IST   Add search form to your site   Submit documents   Feedback  

CiteSeer.IST - Copyright Penn State and NEC