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 back-ground of the biologist is poor. Furthemore 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 complex 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 specific 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 binary 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 illustrated to show graphically the algorithm dynamics.

APLOGEN: Object-oriented genetic algorithm performing Monte Carlo optimization / F.M. Stefanini, A. Camussi. - In: COMPUTER APPLICATIONS IN THE BIOSCIENCES. - ISSN 0266-7061. - 9(1993), pp. 685-700.

APLOGEN: Object-oriented genetic algorithm performing Monte Carlo optimization

F.M. Stefanini
Primo
;
1993

Abstract

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 back-ground of the biologist is poor. Furthemore 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 complex 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 specific 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 binary 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 illustrated to show graphically the algorithm dynamics.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/849401
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