Real-coded memetic algorithms with crossover hill-climbing
Evolutionary Computation - Special issue on magnetic algorithms
Crossover gene selection by spatial location
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Engineering Applications of Artificial Intelligence
Replacement strategies to preserve useful diversity in steady-state genetic algorithms
Information Sciences: an International Journal
Multi-operator based evolutionary algorithms for solving constrained optimization problems
Computers and Operations Research
Mechanism design and analysis of genetic operations in solving traveling salesman problems
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
ACSAC'05 Proceedings of the 10th Asia-Pacific conference on Advances in Computer Systems Architecture
An adaptive evolutionary approach for real-time vehicle routing and dispatching
Computers and Operations Research
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Typical evolutionary algorithms (EAs) exploit the different space-search properties of variation operators, such as crossover, mutation and local optimization. There are also various operators in each element. This paper provides an extensive empirical study on the synergy among multiple crossover operators. We choose a number of different crossover operators in an EA and investigate whether or not their combinations outperform the sole usage of the best crossover operator. The traveling salesman problem and the graph bisection problem were chosen for experimentation. Strong synergy effects were observed in both problems