Microarray data usually contain a large number of genes, but a small number of samples. Feature subset selection for microarray data aims at reducing the number of genes so that useful information can be extracted from the samples. Reducing the dimension of data sets further helps in improving the computational efficiency of the learning model. In this paper, we propose a modified algorithm bas...

In this paper we consider a simplified version of the stock cutting (two-dimensional bin packing) problem. We compare three meta-heuristic algorithms (genetic algorithm (GA), tabu search (TS) and simulated annealing (SA)) when applied to this problem. The results show that tabu search and simulated annealing produce good quality results. This is not the case with the genetic algorithm. The prob...

The Robust Coloring Problem (RCP) is a generalization of the well-known Graph Coloring Problem where we seek for a solution that remains valid when extra edges are added. The RCP is used in scheduling of events with possible last-minute changes and study frequency assignments of the electromagnetic spectrum. This problem has been proved as NP-hard and in instances larger than 30 vertices, meta-...

The clustering problem under the criterion of minimum sum square of errors is a non-convex and nonlinear problem, which possesses many locally optimal values, resulting that its solution often being stuck at locally optimal solution. In this paper, a hybrid genetic, tabu search and k-means algorithm, called GeneticTKM, is proposed for the clustering problem. A new mutation operator is presented...

In previous work solutions for the nesting problem are produced using the no fit polygon (NFP), a new evaluation method and three evolutionary algorithms (simulated annealing (SA), tabu search (TS) and genetic algorithms (GA)). Tabu search has been shown to produce the best quality solutions for two problems. In this paper this work is developed. A relatively new type of search algorithm (ant a...

Combined the global optimization ability of particle swarm algorithm and memory capacity of tabu algorithm, this paper proposed an automatic vector road network matching method based on the combination of particle swarm optimization and tabu strategy. Firstly, the similarity between node entities is evaluated by means of geometric and topological characteristics. Then, the basic principle of gl...

Optimal routing in communication networks is a major issue to be solved. In this paper, the application of Tabu Search (TS) in the optimum routing problem where the aim is to minimize the computational time and improvement of quality of the solution in the communication have been addressed. The goal is to minimize the average delays in the communication. The effectiveness of Tabu Search method ...

The clustering problem under the criterion of minimum sum square of errors is a non-convex and non-linear problem, which possesses many locally optimal values, resulting that its solution often being stuck at locally optimal solution. In this paper, a hybrid genetic, tabu search and k-means algorithm, called GeneticTKM, is proposed for the clustering problem. A new mutation operator is presente...

We study and compare asynchronous parallelization strategies for tabu search, and evaluate the impact on performance and solution quality of some important algorithmic design parameters: number of processors, handling of exchanged information, etc. Parallelization approaches are implemented and compared by using a tabu search algorithm for multicommodity location-allocation problems with balanc...

The genomic median problem is an optimization problem inspired by a biological issue: it aims to find the chromosome organization of the common ancestor to multiple living species. It is formulated as the search for a genome that minimizes a rearrangement distance measure among given genomes. Several attempts have been reported for solving this NP-hard problem. These range from simple heuristic...