Abstract
At an abstract level, memetic algorithms can be seen as a broad class of populationbased stochastic local search (SLS) methods, where a main theme is “exploiting allavailableknowledge about a problem,” see also Moscato and Cotta [618], page 105. The most wide-spread implementation of this theme is probably that of improving some or all individuals in the population by some local search method. This combination of a population-based, global search and a single-solution local search is a very appealing one. The global search capacity of the evolutionary part of a memetic algorithm takes care of exploration, trying to identify the most promising search space regions; the local search part scrutinizes the surroundings of some initial solution, exploiting it in this way. This idea is not only an appealing one, it is also practically a very successful one. In fact, for a vast majority of combinatorial optimization problems and, as it is also becoming more clear in recent research, also for many continuous optimization problems this combination leads to some of best performing heuristic optimization algorithms.