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Showing papers on "Extremal optimization published in 1995"


01 Sep 1995
TL;DR: The results indicate that using genetic algorithms for the optimization of static functions does not yield a benefit, in terms of the final answer obtained, over simpler optimization heuristics.
Abstract: This report is a repository of the results obtained from a large scale empirical comparison of seven iterative and evolution-based optimization heuristics. Twenty-seven static optimization problems, spanning six sets of problem classes which are commonly explored in genetic algorithm literature, are examined. The problem sets include job-shop scheduling, traveling salesman, knapsack, binpacking, neural network weight optimization, and standard numerical optimization. The search spaces in these problems range from 2368 to 22040. The results indicate that using genetic algorithms for the optimization of static functions does not yield a benefit, in terms of the final answer obtained, over simpler optimization heuristics. Descriptions of the algorithms tested and the encodings of the problems are described in detail for reproducibility.

134 citations


Journal ArticleDOI
TL;DR: An improved GA which considers more than two groups simultaneously is devised, based on the permutation representation and genetic sequencing operators originally developed for the travelling salesman problem.
Abstract: Describes the development of two genetic algorithm (GA) programs for cost optimization of opportunity‐based maintenance policies. The combinatorial optimization problem is formulated and it is shown that genetic algorithms are particularly suited to this type of problem. The theoretical basis and operations of a standard genetic algorithm (SGA) are presented with an iterative procedure necessary for implementation of the SGA to least‐cost part replacement. However, an SGA used in an iterative manner may limit the global search capability of the evolutionary computing technique and may lead to suboptimal solutions. To avoid this problem, an improved GA which considers more than two groups simultaneously is devised. This model is based on the permutation representation and genetic sequencing operators originally developed for the travelling salesman problem. The same example used with the SGA confirmed that the improved GA can bring additional savings.

29 citations


Proceedings ArticleDOI
29 Nov 1995
TL;DR: The aim is to integrate the approach introduced by Dorigo et al., known as the ant system, with GA, exploiting the cooperative effect of the latter and the evolutionary effect of GA, to optimize another algorithm for optimization.
Abstract: The authors propose the use of genetic algorithms (GA) to optimize another algorithm for optimization. The aim is to integrate the approach introduced by Dorigo et al., known as the ant system, with GA, exploiting the cooperative effect of the latter and the evolutionary effect of GA. An ant algorithm aims to solve problems of combinatorial optimization by means of a population of agents/processors that work parallel without a supervisor in a cooperative manner. A genetic algorithm aims to optimize the performance of the ant population by selecting optimal values for its parameters by means of evolution of the genetic patrimony associated with each single agent. The approach has been applied to the traveling salesman problem; results and comparisons with the original method are presented.

29 citations


Proceedings ArticleDOI
29 Nov 1995
TL;DR: This work applies a two-level genetic algorithm to an advanced transportation problem, an example of the General Pickup and Delivery Problem, and shows that the results scale well with size and that application to real-world situations is within reach.
Abstract: Many transportation problems, such as the travelling salesman problem, are computationally hard but often solvable quickly, although with less certainty, by heuristic methods. Genetic algorithms fall into this category and generate results with favourable scaling behaviour. We apply a two-level genetic algorithm to an advanced transportation problem, an example of the General Pickup and Delivery Problem. We discuss the formulation of the problem as an evolutionary one, show that the results scale well with size and that application to real-world situations is within reach.

19 citations



Journal ArticleDOI
TL;DR: An optimization algorithm, based on Creutz's microcanonical simulation technique, which has proven very efficient for non-convex optimization tasks associated with image-processing applications and should also constitute a useful heuristic for applications in other domains requiring combinatorial optimization searches.

12 citations


Journal ArticleDOI
TL;DR: A new research domain in Operational Research is presented, probabilistic combinatorial optimization problems, and several problems dealing with this domain as well as future research issues are discussed.

12 citations


Journal ArticleDOI
TL;DR: This work proposes the following strategy for solving large distributed query optimization problems in relational database systems: represent each query-processing schedule by a labeled directed graph, reduce the number of different schedules by pruning away invalid or high-cost solutions, and find a suboptimal schedule by combinatorial optimization.
Abstract: In relational distributed databases a query cost consists of a local cost and a transmission cost. Query optimization is a combinatorial optimization problem. As the query size grows, the optimization methods based on exhaustive search become too expensive. We propose the following strategy for solving large distributed query optimization problems in relational database systems: (1) represent each query-processing schedule by a labeled directed graph; (2) reduce the number of different schedules by pruning away invalid or high-cost solutions; and (3) find a suboptimal schedule by combinatorial optimization. We investigate several combinatorial optimization techniques: random search, single start, multistart, simulated annealing, and a combination of random search and local simulated annealing. The utility of combinatorial optimization is demonstrated in the problem of finding the (sub)optimal semijoin schedule that fully reduces all relations of a tree query. The combination of random search and local simulated annealing was superior to other tested methods.

10 citations