Abstract
In recent years the genetic algorithm (GA) was used successfully to solve many optimization problems. One of the most difficult questions of applying GA to a particular problem is that of coding. In this paper a scheme is derived to optimize one aspect of the coding in an automatic fashion. This is done by using a high cardinality alphabet and optimizing the meaning of the letters. The scheme is especially well suited in cases where a number of similar problems need to be solved. The use of the scheme is demonstrated with such a group of problems: the simplified problem of navigating a ‘robot’ in a ‘room.’ It is shown that for the sample problem family the proposed algorithm is superior to the canonical GA.
Similar content being viewed by others
References
Antonisse J (1989) A new interpretation of schema notation that over-turns the binary encoding constraint. In: Shaffer JD (eds) Proceedings of the third international conference on genetic algorithms. Morgan Kaufmann, New York, pp 86–91
Beasley D, Bull DR, Martin RR (1993) An overview of genetic algorithms. Part 1. Fundamentals. Univ Comput 15:58–69
Collins A, Loftus E (1975) A spreading activation theory of semantic processing. Psychol Rev 82:407–428
Fomin T, Szepesvári Cs, Lörincz A (1994) Self-organizing neurocontrol. In: Proceedings of IEEE International Conference on Neural Networks, Vol 5, Orlando, Florida, pp 2777–2780
Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison Wesley, Reading, Mass
Goldberg DE, Lingle R (1985) Alleles, loci, and the traveling salesman problem. In: Proceedings of an International Conference on Genetic Algorithms and Their Application, pp 154–159
Kalmár Zs, Szepesvári Cs, Lörincz A (1994) Generalization in an autonomous agent. In: Proceedings of IEEE International Conference on Neural Networks, Vol 3, Orlando, Florida, pp 1815–1817
Kenneth E, Kinnear J (1994) Alternatives in automatic function definition: a comparison of performance. In: Kenneth E, Kinnear J (eds) Advances in Genetic Programming, Chap 6. MIT Press, Cambridge, Mass. pp 119–141
Kohonen T (1984) Self-organization and associative memory. Springer, Berlin Heidelberg New York
Oliver IM, Smith DJ, Holland JRC (1987) A study of permutation crossover operators on the traveling salesman problem. In: Genetic algorithms and their applications: Proceedings of the Second International Conference on Genetic Algorithms, pp 224–230
Press WH, Teukolsky SA, Vetterling WT, Flannery BP (1992) Numerical recipes in C: the art of scientific programming, 2nd edn. Cambridge University Press, Cambridge, UK
Ritter H, Martinetz T, Schulten K (1989) Topology conserving maps for learning visuomotor-coordination. Neural Networks 2:159–168
Rudolph G (1994) Convergence analysis of canonical genetic algorithm. IEEE Trans Neural Networks 5:96–101
Szepesvári Cs, Lörincz A (1994) Behavior of an adaptive self-organizing autonomous agent working with cues and competing concepts. Adapt Behav 2:131–160
Thagard P (1989) Explanatory coherence. Behav Brain Sci 12:435–467
Tóth JG, Lörincz A (1993) Genetic algorithm with migration on topology conserving maps. In: Gielen S, Kappen B (eds) Proceedings of the International Conference on Artificial Neural Networks. Springer, Berlin Heidelberg New York, pp 605–608
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Tóth, G.J., Kovács, S. & Lörincz, A. Genetic algorithm with alphabet optimization. Biol. Cybern. 73, 61–68 (1995). https://doi.org/10.1007/BF00199056
Received:
Accepted:
Issue Date:
DOI: https://doi.org/10.1007/BF00199056