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 work describes an enterprise system that provides decision support for the monitoring and management of TACAIR operations in a dynamic battle-space. Data from disparate sources are analyzed to provide enhanced situational awareness, dynamic risk assessment, and asset allocation options for responding to changes in the battlefield environment. The(More)
Tactical air command and control systems must consider a multitude of environmental and operational conditions when reassigning assets, which often results in a lengthy decision process. This paper presents a suite of tools that are intended to compress the kill-chain by providing support for the planning and reassignment of tactical air strike assets.(More)