APLOGEN - AN OBJECT-ORIENTED GENETIC ALGORITHM PERFORMING MONTE-CARLOOPTIMIZATION

Problem-solving and modelling within a biological context often need a level of descriptive accuracy that is unlikely to be capable of analy tical treatment, especially if the mathematical background of the biol ogist 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 complex problems without specifying ru les for intermediate steps. This approach becomes feasible performing a Monte Carlo simulation of the natural evolution process, in which po pulation improvement (search for solutions) in a considered environmen t (the specific problem domain) is achieved by following the genetic p aradigm. Starting with a randomly constituted sample of individuals, d rawn 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 const raints violated by each individual becomes smaller. During the constit ution 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 independen t of a specific context. We have developed the constant core of a mini mal genetic algorithm, from which can be derived genetic problem-solve rs in specific domains. An applicative example-a constrained matrix eq uation on signed integers-is also realized to show graphically the alg orithm dynamics.