No abstract

Abstract. The standard Homogeneity-Based (SHB) optimization algorithm is a metaheuristic which is proposed based on a simultaneously balance between fitting and generalization of a given classification system. However, the SHB algorithm does not penalize the structure of a classification model. This is due to the way SHB's objective function is defined. Also, SHB algorithm uses only genetic algorithm to tune its parameters. This may reduce SHB's freedom degree. In this paper we have proposed an Improved Homogeneity-Based Algorithm (IHBA) which adopts computational complexity of the used data mining approach. Additionally, we employs several metaheuristics to optimally find SHB's parameters values. In order to prove the feasibility of the proposed approach, we conducted a computational study on some benchmarks datasets obtained from UCI repository. Experimental results confirm the theoretical analysis and show the effectiveness of the proposed IHBA method.

No abstract

No abstract

The increasing interest in developing efficient and effective optimization techniques has conducted researchers to turn their attention towards biology. It has been noticed that biology offers many clues for designing novel optimization techniques, these approaches exhibit self-organizing capabilities and permit the reachability of promising solutions without the existence of a central coordinator. In this paper we handle the problem of dynamic web service composition, by using the clonal selection algorithm. In order to assess the optimality rate of a given composition, we use the QOS attributes of the services involved in the workflow as well as, the semantic similarity between these components. The experimental evaluationshows that the proposed approach has a better performance in comparison with other approaches such as the genetic algorithm.