Robert G. Reynolds

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This paper describes the simulated car racing competition held in association with the IEEE WCCI 2008 conference. The organization of the competition is described, along with the rules, the software used, and the submitted car racing controllers. The results of the competition are presented, followed by a discussion about what can be learned from this(More)
This paper introduce a cultural algorithm based testbed which allows one to plug and play various combinations of evolution components for solving constrained numerical optimization. Our cultural algorithm framework combines weak search method with knowledge representation scheme for collecting and reasoning knowledge about individual experience. Currently(More)
In this paper, the advantages of a fuzzy representation in problem solving and search is investigated using the framework of Cultural algorithms (CAs). Since all natural languages contain a fuzzy component, the natural question is "Does this fuzzy representation facilitate the problem-solving process, within these systems". In order to investigate this(More)
Our previous work on real-valued function optimization problems had shown that cultural learning emerged as the result of meta-level interaction or swarming of knowledge sources, “knowledge swarms” in the belief space. These meta-level swarms induced the swarming of individuals in the population space, “Cultural Swarms”. The interaction of these knowledge(More)
A mathematical framework for digital halfloning is proposed. Two models for digital halftoning are provided, one based on maximum-entropy Gibbs measures and one based on reversible Markov chains. The models are seen to be equivalent. This equivalence induces an equivalence between two associated halftoning algorithms, one based on neural networks and one(More)