This paper describes a method for optimizing the cost matrix of any approximate string matching algorithm based on the Levenshtein distance. The method, which uses genetic algorit...
This paper argues that multiagent learning is a potential “killer application” for generative and developmental systems (GDS) because key challenges in learning to coordinate ...
Given a set of strings S of equal lengths over an alphabet Σ, the closest string problem seeks a string over Σ whose maximum Hamming distance to any of the given strings is as s...
This paper is concerned with a specific brand of evolutionary algorithms: Memetic algorithms. A new local search technique with an adaptive neighborhood setting process is introdu...
In many design tasks it is difficult to explicitly define an objective function. This paper uses machine learning to derive an objective in a feature space based on selected examp...