Is a comparison of results meaningful from the inexact replications of computational experiments?

The main objective of this paper is to correct the unreasonable and inaccurate criticism to our previous experiments using Teaching–Learning-Based Optimization algorithm and to quantify the amount of error that may arise due to incorrect counting of fitness evaluations. It is shown that inexact experiment replication should be avoided in comparisons between meta-heuristic algorithms whenever possible. Otherwise, an inexact replication and margin of error should be explicitly reported

Domain-specific languages as key tools for ULSSIS engineering

We briefly discuss the potential of domain-specific languages and domain-specific modeling languages for ULSSIS engineering, some of the scaling challenges involved, and the possibilities for raising expressiveness beyond current levels

Domain-specific languages in perspective

Domain-specific languages (DSLs) are languages tailored to a specific application domain. They offer substantial gains in expressiveness and ease of use compared with general-purpose languages in their domain of application. Although the use of DSLs is by no means new, it is receiving increased attention in the context of model-driven engineering and development of parallel software for multicore processors. We discuss these trends from the perspective of the roles DSLs have traditionally played

Hybridization of Evolutionary Algorithms

Evolutionary algorithms are good general problem solver but suffer from a lack of domain specific knowledge. However, the problem specific knowledge can be added to evolutionary algorithms by hybridizing. Interestingly, all the elements of the evolutionary algorithms can be hybridized. In this chapter, the hybridization of the three elements of the evolutionary algorithms is discussed: the objective function, the survivor selection operator and the parameter settings. As an objective function, the existing heuristic function that construct the solution of the problem in traditional way is used. However, this function is embedded into the evolutionary algorithm that serves as a generator of new solutions. In addition, the objective function is improved by local search heuristics. The new neutral selection operator has been developed that is capable to deal with neutral solutions, i.e. solutions that have the different representation but expose the equal values of objective function. The aim of this operator is to directs the evolutionary search into a new undiscovered regions of the search space. To avoid of wrong setting of parameters that control the behavior of the evolutionary algorithm, the self-adaptation is used. Finally, such hybrid self-adaptive evolutionary algorithm is applied to the two real-world NP-hard problems: the graph 3-coloring and the optimization of markers in the clothing industry. Extensive experiments shown that these hybridization improves the results of the evolutionary algorithms a lot. Furthermore, the impact of the particular hybridizations is analyzed in details as well