  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.
