The Rowland Institute for Science

Craig G Shaefer

In one aspect, an optimization method finds the best solution to a problem of the kind for which there is a space of possible solutions; in the method, tokens (e.g., chromosomes) take on values that represent trial solutions in accordance with a representational scheme that defines the relationships between given token values and corresponding trial solutions; by an iterative process, the values of the tokens are changed to explore the solution space and to converge on the best solution; and for at least some iterations, characteristics of the tokens and/or the trial solutions are analyzed and the representational scheme for later iterations is modified based on the analysis for earlier iterations without interrupting the succession of iterations. In another aspect, a set of operators is made available to enable a user to implement any one of at least two different algorithms.

Genetic algorithm

In one aspect, an optimization method finds the best solution to a problem of the kind for which there is a space of possible solutions; in the method, tokens (e.g., chromosomes) take on values that represent trial solutions in accordance with a representational scheme that defines the relationships between given token values and corresponding trial solutions; by an iterative process, the values of the tokens are changed to explore the solution space and to converge on the best solution; and for at least some iterations, characteristics of the tokens and/or the trial solutions are analyzed and the representational scheme for later iterations is modified based on the analysis for earlier iterations without interrrupting the succession of iterations. In another aspect, a set of operators is made available to enable a user to implement any one of at least two different algorithms.

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Charlestown, MA, United States of America

Craig G Shaefer