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While previous work on case injected genetic algorithms has shown an improvement in solution quality and time to solution when solving a sequence of similar problems, we believe there has been no prior investigation of using case injection to intentionally alter convergence away from the most fit solution and toward another slightly suboptimal solution that is favored for reasons external to the numerical problem formulation (for example, based on similarity to a human expert's solution). We investigate the use of case injection to bias the results of a genetic algorithm (GA) toward a desired but slightly suboptimal solution, 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 the injected cases were gathered. In the first scenario, we find that injection of cases does lead to preferential convergence to solutions similar to the runs from which the cases are gathered. We find that the more similar the injected problem is to the problem from which the cases were gathered, the more marked is the solution bias effect, though the technique can still be used to bias GA results even when the problems differ markedly. This technique has application where we wish a GA to derive solutions similar to (for example) known "good" solutions or human derived solutions, when, because of incomplete modeling information, the numerical formulation of the problem itself and its fitness function do not necessarily contain all information about the problem. This has potential applications in human modeling and in developing quality opponents in gaming applications.
Evolutionary Computation, 2003. CEC '03. The 2003 Congress on (Volume:2 )
Date of Conference: 8-12 Dec. 2003