Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
A genetic approach to the quadratic assignment problem
Computers and Operations Research - Special issue on genetic algorithms
Computers and Operations Research
A greedy genetic algorithm for the quadratic assignment problem
Computers and Operations Research
Discrete and Combinatorial Mathematics: An Applied Introduction
Discrete and Combinatorial Mathematics: An Applied Introduction
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Adaptive memories for the Quadratic Assignment Problems
Adaptive memories for the Quadratic Assignment Problems
A New Genetic Algorithm for the Quadratic Assignment Problem
INFORMS Journal on Computing
Computers and Operations Research
Enhancing the performance of hybrid genetic algorithms by differential improvement
Computers and Operations Research
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In this paper we propose an improvement to the widely used metaheuristic genetic algorithm. We suggest a change in the way parents are selected. The method is based on examining the similarity of parents selected for mating. Computational comparisons for solving the quadratic assignment problem using a hybrid genetic algorithm demonstrate the effectiveness of the method. This conclusion is examined statistically. We also report extensive computational results of solving the quadratic assignment problem Tho150. The best variant found the best known solution 8 times out of 20 replications. The average value of the objective function was 0.001% over the best known solution. Run time for this variant is about 18 hours per replication. When run time is increased to about two days per replication the best known solution was found 7 times out of 10 replications with the other three results each being 0.001% over the best known.