Coevolutionary bid-based genetic programming for problem decomposition in classification

In this work a cooperative, bid-based, model for problem decomposition is proposed with application to discrete action domains such as classification. This represents a significant departure from models where each individual constructs a direct inputoutcome map, for example, from the set of exemplars to the set of class labels as is typical under the classification domain. In contrast, the proposed model focuses on learning a bidding strategy based on the exemplar feature vectors; each individual is associated with a single discrete action and the individual with the maximum bid `wins' the right to suggest its action. Thus, the number of individuals associated with each action is a function of the intra-action bidding behaviour. Credit assignment is designed to reward correct but unique bidding strategies relative to the target actions. An advantage of the model over other teaming methods is its ability to automatically determine the number of and interaction between cooperative ...

• Type: Journal
• Year: 2008
• Where: GPEM
• Authors: Peter Lichodzijewski, Malcolm I. Heywood
• Keywords: Classifier Systems, GPEM 2008, Model, Support Vector Machines