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© Oxford University Press

APLOGEN: an object-oriented genetic algorithm performing Monte Carlo optimization

Federico M. Stefanini 1 and Alessandro Camussi

Istauto Agronomico per I'Oltremare, Ministero degli Affari Esteri Via Cocchi 4,50131 Florence and Canedra di Genenca Agraria, Università degli Studi di Firenze, Istituto di Selvicoltura via S. Bonaveniura 13, 50145 Florence, Italy

1To whom reprint requests should be sent. Present address: Istituro di Selvicoltura, Università degli Studi di Firenze, via S. Bonaventura 13, 50145 Florence, Italy

Problem-solving and modelling within a biological context often need a level of descriptive accuracy that is unlikely to be capable of analytical treatment, especially if the mathematical background of the biologist is poor. Furthermore solver-model maintenance is often difficult without the availability of trained specialists. Better prospects are found in the genetic algorithm field. Genetic algorithms are a set of procedures formulated to solve cotnplex problems without specifying rules for intermediate steps. This approach becomes feasible performing a Monte Carlo simulation of the natural evolution process, in which population improvement (search for solutions) in a considered environment (the spec problem domain) is achieved by following the genetic paradigm. Starting with a randomly constituted sample of individuals, drawn from the population of admissible values and expressed as binaty strings, random mating brings about individuals of the next generation. Parents are chosen with a greater probability as the number of constraints violated by each individual becomes smaller. During the constitution of each generation the presence of some genetic operators causes the improvement of population diversity and its maintenance. Genetic operators are simple string transformation rules, generally independent of a specific context. We have developed the constant core of a minimal genetic algorithm, from which can be derived genetic problem-solvers in specific domains. An applicative example—a constrained matrix equation on signed integers—is also realized to show graphically the algorithm dynamics.

Received on March 10, 1993; accepted on August 3, 1993

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