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Simple models of coevolutionary genetic algorithms (2001)
Journal Article
Bull, L. (2001). Simple models of coevolutionary genetic algorithms. Artificial Life and Robotics, 5(1), 58-66. https://doi.org/10.1007/BF02481321

The use of evolutionary computing techniques in coevolutionary/multiagent systems is becoming increasingly popular. This paper presents some simple models of the genetic algorithm in such systems, with the aim of examining the effects of different ty... Read More about Simple models of coevolutionary genetic algorithms.

Coevolving functions in genetic programming (2001)
Journal Article
Ahluwalia, M., & Bull, L. (2001). Coevolving functions in genetic programming. Journal of Systems Architecture, 47(7), 573-585. https://doi.org/10.1016/S1383-7621%2801%2900016-9

In this paper we introduce a new approach to the use of automatically defined functions (ADFs) within genetic programming. The technique consists of evolving a number of separate sub-populations of functions which can be used by a population of evolv... Read More about Coevolving functions in genetic programming.

Simple Markov models of the genetic algorithm in classifier systems: multi-step tasks (2001)
Journal Article
Bull, L. (2001). Simple Markov models of the genetic algorithm in classifier systems: multi-step tasks. Lecture Notes in Artificial Intelligence, 1996, 29-36

Abstract Michigan-style Classifier Systems use Genetic Algorithms to facilitate rule discovery. This paper presents a simple Markov model of the algorithm in such systems, with the aim of examining the effects of different types of interdependence be... Read More about Simple Markov models of the genetic algorithm in classifier systems: multi-step tasks.

A self-adaptive classifier system (2001)
Conference Proceeding
Hurst, J., & Bull, L. (2001). A self-adaptive classifier system. In P. L. Lanzi, W. Stolzmann, & S. W. Wilson (Eds.), Advances in Learning Classifier Systems. , (70-79). https://doi.org/10.1007/3-540-44640-0_6

© Springer-Verlag Berlin Heidelberg 2001. The use and benefits of self-adaptive parameters, particularly mutation, are well-known within evolutionary computing. In this paper we examine the use of parameter self-adaptation in Michigan-style Classifie... Read More about A self-adaptive classifier system.