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| The combination of local search heuristics and genetic algorithms is a promising approach for nding near-optimum solutions to the traveling salesman problem (TSP). In this paper, an approach is presented in which local search techniques are used to nd local optima in a given TSP search space, and genetic algorithms are used to search the space of local(More)
The fitness landscape of the graph bipartitioning problem is investigated by performing a search space analysis for several types of graphs. The analysis shows that the structure of the search space is significantly different for the types of instances studied. Moreover, with increasing epistasis, the amount of gene interactions in the representation of a(More)
In this paper, an approach is presented to incorporate problem speciic knowledge into a genetic algorithm which is used to compute near-optimum solutions to traveling salesman problems (TSP). The approach is based on using a tour construction heuristic for generating the initial population, a tour improvement heuristic for nding local optima in a given TSP(More)
Memetic algorithms (MAs) have demonstrated very effective in combinatorial optimization. This paper offers explanations as to why this is so by investigating the performance of MAs in terms of efficiency and effectiveness. A special class of MAs is used to discuss efficiency and effectiveness for local search and evolutionary meta-search. It is shown that(More)
The notion of tness landscapes has been introduced to describe the dynamics of evolutionary adaptation in nature 40] and has become a powerful concept in evolutionary theory. Fitness landscapes are equally well suited to describe the behavior of heuristic search methods in optimization, since the process of evolution can be thought of as searching a(More)