The Effects of Memes on Memetic Algorithms for Solving Quadratic Assignment Problem The quadratic assignment problem (QAP) is known as one of NPhard combinatorial optimization problems where a set of facilities must be assigned to a set of locations in order to minimize total cost. In this paper, we present the effect of local search algorithm referred to as meme on Memetic Algorithms (MAs). We also compare four different local search metaheuristics: Hill Climbing Algorithm (HC), Tabu Search (TS), Simulated Annealing (SA), and Iterated Local Search (ILS) for solving QAP and analyze their performance in terms of solution quality. The results show that ILS is the best metaheuristic followed by SA, TS, and HC, respectively. While the MA using ILS as a meme is the best among all four MAs, the MA using SA as a meme is not the secondbest metaheuristic, but the worst among all.

The core concept for the multidimensional knapsack problem We present the newly developed core concept for the Multidimensional Knapsack Problem (MKP) which is an extension of the classical concept for the onedimensional case. The core for the multidimensional problem is defined in dependence of a chosen efficiency function of the items, since no single obvious efficiency measure is available for MKP. An empirical study on the cores of widelyused benchmark instances is presented, as well as experiments with different approximate core sizes. Furthermore we describe a memetic algorithm and a relaxation guided variable neighborhood search for the MKP, which are applied to the original and to the core problems. The experimental results show that given a fixed runtime, the different metaheuristics as well as a general purpose integer linear programming solver yield better solution when applied to approximate core problems of fixed size.