Showing papers on "Genetic algorithm published in 1986"
01 Jan 1986
TL;DR: GA's are shown to be effective for both levels of the systems optimization problem and are applied to the second level task of identifying efficient GA's for a set of numerical optimization problems.
Abstract: The task of optimizing a complex system presents at least two levels of problems for the system designer. First, a class of optimization algorithms must be chosen that is suitable for application to the system. Second, various parameters of the optimization algorithm need to be tuned for efficiency. A class of adaptive search procedures called genetic algorithms (GA) has been used to optimize a wide variety of complex systems. GA's are applied to the second level task of identifying efficient GA's for a set of numerical optimization problems. The results are validated on an image registration problem. GA's are shown to be effective for both levels of the systems optimization problem.
2,924 citations
01 Jan 1986
TL;DR: The application of a genetic algorithm (GA) to the optimal design of a ten member, plane truss is considered and computer results show surprising speed as near-optimal results are obtained after examining a small fraction of the search space.
Abstract: The application of a genetic algorithm (GA) to the optimal design of a ten member, plane truss is considered. Genetic algorithms are search procedures based upon the mechanics of natural genetics, combining a Darwinian survival-of-the-fittest with a randomized, yet structured information exchange among a population of artificial chromosomes. Computer results show surprising speed as near-optimal results are obtained after examining a small fraction of the search space. The method is ready for application to more complex problems of engineering optimization.
339 citations
Journal Article•
TL;DR: In this article, the application of a GA to the optimal design of a ten member, plane truss is considered, and results show surprising speed as near-optimal results are obtained after examining a small fraction of the search space.
Abstract: The application of a genetic algorithm (GA) to the optimal design of a ten member, plane truss is considered. Genetic algorithms are search procedures based upon the mechanics of natural genetics, combining a Darwinian survival-of-the-fittest with a randomized, yet structured information exchange among a population of artificial chromosomes. Computer results show surprising speed as near-optimal results are obtained after examining a small fraction of the search space. The method is ready for application to more complex problems of engineering optimization.
309 citations
TL;DR: The three genetic operators which are the core of the reproductive design are detailed, and an algorithm is presented to illustrate applications to discrete-space location problems, particularly thep-median.
Abstract: Genetic algorithms are adaptive sampling strategies based on information processing models from population genetics. Because they are able to sample a population broadly, they have the potential to out-perform existing heuristics for certain difficult classes of location problems. This paper describes reproductive plans in the context of adaptive optimization methods, and details the three genetic operators which are the core of the reproductive design. An algorithm is presented to illustrate applications to discrete-space location problems, particularly thep-median. The algorithm is unlikely to compete in terms of efficiency with existingp-median heuristics. However, it is highly general and can be fine-tuned to maximize computational efficiency for any specific problem class.
155 citations