159 research outputs found
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
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