Local Search

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 that of “exploiting all available knowledge about a problem,” see also Moscato and Cotta [35], page 105. The probably most wide-spread implementation of this theme is 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 a practically 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 most performing heuristic optimization algorithms. The role of the local search is fundamental and the selection of its search rule and its harmonization within the global search schemes make the global algorithmic success of memetic frameworks. The local search can be integrated within the evolutionary cycle mainly in two ways. The first is the so called “life-time learn-

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