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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)
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)
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)
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)
A memetic algorithm (MA), i.e. an evolutionary algorithm making use of local search, for the quadratic assignment problem is presented. A new recombination operator for realizing the approach is described, and the behavior of the MA is investigated on a set of problem instances containing between 25 and 100 facilities/locations. The results indicate that(More)
In this paper, two types of tness landscapes of the graph bi-partitioning problem are analyzed, and a memetic algorithm { a genetic algorithm incorporating local search { that nds near-optimum solutions eeciently is presented. A search space analysis reveals that the tness landscapes of geometric and non-geometric random graphs diier significantly , and(More)