Trusses, NP-Completeness, and Genetic Algorithms
نویسندگان
چکیده
The optimization of large trusses often leads to a nearly optimal solution, rather than a truly optimal design. In fact, the problem space for truss optimization grows exponentially with the size of the truss. Using the method of problem reduction, this paper demonstrates that truss optimization is in the set of NP-complete problems. Hence, the only practical techniques for solving the truss problem are heuristic in nature. Genetic algorithms provide a viable solution for large trusses.
منابع مشابه
THE EFFECTS OF INITIAL SAMPLING AND PENALTY FUNCTIONS IN OPTIMAL DESIGN OF TRUSSES USING METAHEURISTIC ALGORITHMS
Although Genetic algorithm (GA), Ant colony (AC) and Particle swarm optimization algorithm (PSO) have already been extended to various types of engineering problems, the effects of initial sampling beside constraints in the efficiency of algorithms, is still an interesting field. In this paper we show that, initial sampling with a special series of constraints play an important role in the conv...
متن کاملNP-Completeness of Deciding Binary Genetic Encodability
In previous work of the second author a rigorous mathematical foundation for re-encoding one evolutionary search algorithm by another has been developed. A natural issue to consider then is the complexity of deciding whether or not a given evolutionary algorithm can be re-encoded by one of the standard classical evolutionary algorithms such as a binary genetic algorithm. In the current paper we...
متن کاملSequential and Parallel Algorithms for the Shortest Common Superstring Problem
We design sequential and parallel genetic algorithms, simulated annealing algorithms and improved greedy algorithms for the shortest common superstring problem(SCS), which is to find the shortest string that contains all strings from a given set of strings. The SCS problem is NP-complete [7]. It is even MAX SNP hard [2] i.e. no polynomial-time algorithm exists, that can approximate the optimum ...
متن کاملModified Genetic Algorithms Based Solution to Subset Sum Problem
Subset Sum Problem (SSP) is an NP Complete problem which finds its application in diverse fields. The work suggests the solution of above problem with the help of genetic Algorithms (GAs). The work also takes into consideration, the various attempts that have been made to solve this problem and other such problems. The intent is to develop a generic methodology to solve all NP Complete problems...
متن کاملUsing Neural Networks and Genetic Algorithms as Heuristics for NP-Complete Problems
USING NEURAL NETWORKS AND GENETIC ALGORITHMS AS HEURISTICS FOR NP-COMPLETE PROBLEMS William M. Spears, M.S. George Mason University, 1989 Thesis Director: Dr. Kenneth A. De Jong Paradigms for using neural networks (NNs) and genetic algorithms (GAs) to heuristically solve boolean satisfiability (SAT) problems are presented. Results are presented for two-peak and false-peak SAT problems. Since SA...
متن کامل