John R. McDonnell

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This paper presents a new approach to genetic algorithm based machine learning. We use genetic algorithms augmented with a case-based memory of past problem solving attempts to obtain better performance over time on sets of similar problems. Rather than starting anew on each problem, we periodically inject a genetic algorithm's population with appropriate(More)
We investigate the use of case injection to bias the results of a genetic algorithm (GA) in two scenarios. First, when the problem we are attempting to bias by case injection is identical to the problem from which the injected cases were gathered. Second, when the problem we are attempting to bias is different (to varying degree) from the problem from which(More)
This paper discusses the use of evolutionary computation for an automated player of a real-time strategic tactics game in which assets are assigned to targets and threats belonging to the opposing team. Strategy games such as this are essentially a series of asset allocation problems to which evolutionary algorithms are particularly adept. This game(More)
This paper describes decision support tools that are being developed to support dynamic reallocation of tactical air assets in particular and the strike force assets in general. Tools that support situation awareness, risk assessment, and weapon-target paring options generation in an integrated architecture are discussed. Preliminary work is presented on(More)