Summary
This article investigates case-initialized genetic algorithms for extracting knowledge from past problem solving to solve subsequent problems. We develop a test problem class with similar solutions and the genetic algorithm is run for randomly chosen problems from the class. As the algorithm runs on a particular problem, solution strings are stored in a case-base and on subsequent problems, solutions from the case-base are used to initialize the population of a genetic algorithm. We investigate the effect of selection strategy and choice of appropriate cases for injection. Scaled roulette and scaled elitist selection both show improvement over a randomly initialized GA and elitist selection performs better than roulette. Over 50 problems the case-initialized genetic algorithm system shows a statistically significant decrease in the time taken to the best solution and solutions are of a higher fitness. Several strategies for choosing cases from the case base for injection all provide measurable improvement over random initialization.
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Johnson, J., Louis, S.J. (2005). Case-Initialized Genetic Algorithms for Knowledge Extraction and Incorporation. In: Jin, Y. (eds) Knowledge Incorporation in Evolutionary Computation. Studies in Fuzziness and Soft Computing, vol 167. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44511-1_4
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DOI: https://doi.org/10.1007/978-3-540-44511-1_4
Publisher Name: Springer, Berlin, Heidelberg
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