All Outputs (4)

Evolutionary computing in multi-agent environments: Operators (1998)
Conference Proceeding
Bull, L. (1998). Evolutionary computing in multi-agent environments: Operators. In V. W. Porto, N. Saravanan, D. Waagen, & A. E. Eiben (Eds.), In EP 1998: Evolutionary Programming VII. , (43-52). https://doi.org/10.1007/BFb0040758

This paper examines a key aspect of applying evolutionary computing techniques to multi-agent systems: a comparison in the performance of the genetic operators of mutation and recombination. Using the tuneable NKC model of multi-agent evolution it is... Read More about Evolutionary computing in multi-agent environments: Operators.

On ZCS in multi-agent environments (1998)
Conference Proceeding
Bull, L. (1998). On ZCS in multi-agent environments. In A. Eiben, T. Bäck, M. Schoenauer, & H. Schwefel (Eds.), Parallel Problem Solving from Nature—PPSN V. , (471-480). https://doi.org/10.1007/BFb0056889

This paper examines the performance of the ZCS Michigan-style classifier system in multi-agent environments. Using an abstract multi-agent model the effects of varying aspects of the performance, reinforcement and discovery components are examined. I... Read More about On ZCS in multi-agent environments.

A corporate classifier system (1998)
Conference Proceeding
Tomlinson, A., & Bull, L. (1998). A corporate classifier system. In A. E. Eiben, T. Bäck, M. Schoenauer, & H. Schwefel (Eds.), Parallel Problem Solving from Nature—PPSN V. , (550-559). https://doi.org/10.1007/BFb0056897

Based on the proposals of Wilson and Goldberg we introduce a macro-level evolutionary operator which creates structural links between rules in the ZCS model and thus forms "corporations" of rules within the classifier system population. Rule co-depen... Read More about A corporate classifier system.

An evolution strategy and genetic algorithm hybrid: An initial implementation and first results (1994)
Conference Proceeding
Bull, L., & Fogarty, T. C. (1994). An evolution strategy and genetic algorithm hybrid: An initial implementation and first results. In T. C. Fogarty (Ed.), In AISB EC 1994: Evolutionary Computing. , (95-102). https://doi.org/10.1007/3-540-58483-8_8

Evolution Strategies (ESs)[15] and Genetic Algorithms (GAs)[13] have both been used to optimise functions, using the natural process of evolution as inspiration for their search mechanisms. The ES uses gene mutation as it’s main search operator whils... Read More about An evolution strategy and genetic algorithm hybrid: An initial implementation and first results.