Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
An algorithm for the three-index assignment problem
Operations Research
Perspectives of Monge properties in optimization
Discrete Applied Mathematics
Computational Optimization and Applications
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Dynamic Control of Genetic Algorithms Using Fuzzy Logic Techniques
Proceedings of the 5th International Conference on Genetic Algorithms
Establishing motion correspondence using extended temporal scope
Artificial Intelligence
Randomized parallel algorithms for the multidimensional assignment problem
Applied Numerical Mathematics - Numerical algorithms, parallelism and applications
Test Problem Generator for the Multidimensional Assignment Problem
Computational Optimization and Applications
GRASP with Path Relinking for Three-Index Assignment
INFORMS Journal on Computing
Nonlinear Assignment Problems: Algorithms and Applications (Combinatorial Optimization)
Nonlinear Assignment Problems: Algorithms and Applications (Combinatorial Optimization)
The gambler's ruin problem, genetic algorithms, and the sizing of populations
Evolutionary Computation
Dynamically tuning the population size in particle swarm optimization
Proceedings of the 2008 ACM symposium on Applied computing
Adaptive and Multilevel Metaheuristics (Studies in Computational Intelligence)
Adaptive and Multilevel Metaheuristics (Studies in Computational Intelligence)
Discrete Applied Mathematics
A Memetic Algorithm for the Multidimensional Assignment Problem
SLS '09 Proceedings of the Second International Workshop on Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics
Target tracking with distributed sensors: The focus of attention problem
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
Metaheuristics: From Design to Implementation
Metaheuristics: From Design to Implementation
A memetic algorithm for the generalized traveling salesman problem
Natural Computing: an international journal
Local search heuristics for the multidimensional assignment problem
Journal of Heuristics
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part IV
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
A tutorial for competent memetic algorithms: model, taxonomy, and design issues
IEEE Transactions on Evolutionary Computation
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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 the population size is one of the most important among them. Most of the 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 runtimes. In this paper we propose an adjustable population size. It is calculated as a function of the runtime of the whole algorithm and the average runtime of the local search for the given instance. Note that in many applications the runtime of a heuristic should be limited and, therefore, we use this bound as a parameter of the algorithm. The average runtime of the local search procedure is measured during the algorithm's run. Some coefficients which are independent of the instance and the local search are to be tuned at the design time; we provide a procedure to find these coefficients. The proposed approach was used to develop a memetic algorithm for the multidimensional assignment problem (MAP). We show that our adjustable population size makes the algorithm flexible to perform efficiently for a wide range of running times and local searches and this does not require any additional tuning of the algorithm.