Eneko Osaba
ABSTRACT
This paper examines the influence of neutral crossover operators in a genetic algorithm (GA) applied to the one-dimensional bin packing problem. In the experimentation 16 benchmark instances have been used and the results obtained by three different GAs are compared with the ones obtained by an evolutionary algorithm (EA). The aim of this work is to determine whether an EA (with no crossover functions) can perform similarly to a GA.
- M. J. Brusco, H. F. Köhn, and D. Steinley. Exact and approximate methods for a one-dimensional minimax bin-packing problem. Annals of Operations Research, 206(1):611--626, 2013.Google Scholar
Cross Ref
- L. Davis. Applying adaptive algorithms to epistatic domains. In International joint conference on artificial intelligence, volume 1, pages 161--163, 1985. Google ScholarDigital Library
- J. H. Holland. Adaptation in natural and artificial systems. Michigan Press, 1975. Google ScholarDigital Library
- S. Martello and P. Toth. Knapsack problems. Algorithms and Computer Implementations. John Wiley & Sons, 1990. Google ScholarDigital Library
- E. Osaba, R. Carballedo, F. Diaz, and A. Perallos. Analysis of the suitability of using blind crossover operators in genetic algorithms for solving routing problems. In Symposium on Applied Computational Intelligence and Informatics, pages 17--22, 2013.Google ScholarCross Ref
- E. Osaba, F. Diaz, and E. Onieva. Golden ball: a novel meta-heuristic to solve combinatorial optimization problems based on soccer concepts. Applied Intelligence, pages 1--22, 2014. Google ScholarDigital Library
- E. Osaba, E. Onieva, R. Carballedo, F. Diaz, A. Perallos, and X. Zhang. A multi-crossover and adaptive island based population algorithm for solving routing problems. Journal of Zhejiang University SCIENCE C, 14(11):815--821, 2013.Google ScholarCross Ref
- S. Swain and D. Mohapatra. Genetic algorithm-based approach for adequate test data generation. In Intelligent Computing, Networking, and Informatics, pages 453--462. Springer, 2014.Google ScholarCross Ref
- G. Syswerda. Schedule optimization using genetic algorithms. Handbook of genetic algorithms, pages 332--349, 1991.Google Scholar
Index Terms
- A study on the efficiency of neutral crossover operators in genetic algorithms applied to the bin packing problem
Recommendations
Hybrid crossover operators for real-coded genetic algorithms: an experimental study
Most real-coded genetic algorithm research has focused on developing effective crossover operators, and as a result, many different types have been proposed. Some forms of crossover operators are more suitable to tackle certain problems than others, ...
Neural network crossover in genetic algorithms using genetic programming
AbstractThe use of genetic algorithms (GAs) to evolve neural network (NN) weights has risen in popularity in recent years, particularly when used together with gradient descent as a mutation operator. However, crossover operators are often omitted from ...
Breaking ties with secondary fitness in a genetic algorithm for the bin packing problem
GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computationIn a genetic algorithm with integer fitnesses, ties in selection tournaments result in the selection of parent chromosomes effectively at random. A secondary measure that estimates how likely chromosomes are to be improved by the GA's variation ...
Comments