Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A New Approach to Population Sizing for Memetic Algorithms: A Case Study
  for the Multidimensional Assignment Problem   Memetic Algorithms are known to be a powerful technique in solving hard
optimization problems. To design a memetic algorithm one needs to make a host
of decisions; selecting a population size is one of the most important among
them. Most algorithms in the literature fix the population size to a certain
constant value. This reduces the algorithm's quality since the optimal
population size varies for different instances, local search procedures and
running times. In this paper we propose an adjustable population size. It is
calculated as a function of the running time of the whole algorithm and the
average running time of the local search for the given instance. Note that in
many applications the running time of a heuristic should be limited and
therefore we use this limit as a parameter of the algorithm. The average
running time of the local search procedure is obtained during the algorithm's
run. Some coefficients which are independent with respect to the instance or
the local search are to be tuned before the algorithm run; we provide a
procedure to find these coefficients. The proposed approach was used to develop
a memetic algorithm for the Multidimensional Assignment Problem (MAP or s-AP in
the case of s dimensions) which is an extension of the well-known assignment
problem. MAP is NP-hard and has a host of applications. We show that using
adjustable population size makes the algorithm flexible to perform well for
instances of very different sizes and types and for different running times and
local searches. This allows us to select the most efficient local search for
every instance type. The results of computational experiments for several
instance families and sizes prove that the proposed algorithm performs
efficiently for a wide range of the running times and clearly outperforms the
state-of-the art 3-AP memetic algorithm being given the same time.

35
The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph
34
The categories of this paper are: chem-ph
8
The categories of this paper are: hep-ex
29
The categories of this paper are: funct-an
0
The categories of this paper are: hep-ph
15
The categories of this paper are: nucl-ex
22
The categories of this paper are: chao-dyn
24
The categories of this paper are: dg-ga
19
The categories of this paper are: eess
4
The categories of this paper are: cond-mat
33
The categories of this paper are: plasm-ph
9
The categories of this paper are: nlin
30
The categories of this paper are: q-alg
18
The categories of this paper are: econ