Evaluating the Seeding Genetic Algorithm
In this paper, we present new experimental results supporting the Seeding Genetic Algorithm (SGA). We evaluate the algorithm’s performance with various parameterisations, making comparisons to the Canonical Genetic Algorithm (CGA), and use these as guidelines as we establish reasonable parameters for the seeding algorithm. We present experimental results confirming aspects of the theoretical basis, such as the exclusion of the deleterious mutation operator from the new algorithm, and report results on GA-difficult problems which demonstrate the SGA’s ability to overcome local optima and systematic deception.
Keywordsgenetic algorithm evolutionary algorithm seeding genetic algorithm seeding operator
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- 1.Forrest, S., Mitchell, M.: Relative building-block fitness and the building-block hypothesis. In: Whitley, L. (ed.) Foundations of Genetic Algorithms, pp. 109–126 (1993)Google Scholar
- 2.Skinner, C., Riddle, P.: Random search can outperform mutation. In: IEEE Congress on Evolutionary Computation, CEC 2007 (2007)Google Scholar
- 3.Skinner, C.: On the discovery, selection and combination of building blocks in evolutionary algorithms. PhD thesis, Citeseer (2009)Google Scholar
- 4.Meadows, B., Riddle, P., Skinner, C., Barley, M.: Evaluating the seeding genetic algorithm (2013), http://www.cs.auckland.ac.nz/~pat/AI2013-long.pdf
- 5.De Jong, K.A.: Analysis of the behavior of a class of genetic adaptive systems. PhD thesis, University of Michigan Ann Arbor, MI (1975)Google Scholar
- 6.Mitchell, M., Forrest, S.: B. 2.7. 5: Fitness landscapes: Royal road functions. Handbook of evolutionary computation (1997)Google Scholar
- 7.Watson, R.A., Pollack, J.B.: Recombination without respect: Schema combination and disruption in genetic algorithm crossover. In: Proceedings of Genetic and Evolutionary Computation Conference (GECCO), pp. 112–119 (2000)Google Scholar
- 8.Goldberg, D.E.: Simple genetic algorithms and the minimal, deceptive problem. Genetic Algorithms and Simulated Annealing 74 (1987)Google Scholar
- 9.Cohen, P., Kim, J.: A bootstrap test for comparing performance of programs when data are censored, and comparisons to Etzioni’s test. Technical report, University of Massachusetts (1993)Google Scholar