Abstract. For genetic algorithms, new variants of the uniform crossover operator that introduce selective pressure on the recombination stage are proposed. Operator probabilistic rates based approach to genetic algorithms selfconfiguration is suggested. The usefulness of the proposed modifications is demonstrated on benchmark tests and real world problems.Keywords: genetic algorithms, uniform crossover, selective pressure recombination, self-configuration, performance comparison.
IntroductionEvolutionary algorithms (EA), the best known representatives of which are genetic algorithms (GA), are well known optimization techniques based on the principles of natural evolution. Although GAs have been successful in solving many of real world optimization problems, their performance depends on the selection of the GA settings and tuning their parameters. GAs usually use a bit-string solution representation, but other decisions have to be made before the algorithms execution. The design of a GA consists of choosing of variation operators (e.g. recombination and mutation) that will be used to generate new solutions from the current population and the parent selection operator (to decide which members of the population are to be used as inputs to the variation operators), as well as a survival scheme (to decide how the next generation is to be created from the current one and outputs of the variation operators). Additionally, real valued parameters of the chosen settings (the probability of recombination, the level of mutation, etc.) have to be tuned. The process of setting choice and parameter tuning is known as a time-consuming and complicated task. Much research has tried to deal with this problem. Some approaches attempted to determine appropriated setting by experimenting over a set of well-defined functions or through theoretical analysis. Another approach, usually applying terms such as "self-adaptation" or "self-tuning", tries to eliminate the setting process by adapting settings through the algorithm execution.There exist much research devoted to "self-adapted" or "self-tuned" GAs and authors of corresponding papers determine similar ideas in very different ways, all of them aimed at reducing the human expert role in algorithms designing.
For genetic programming algorithms new variants of uniform crossover operators that introduce selective pressure on the recombination stage are proposed. Operators probabilistic rates based approach to GP self-configuration is suggested. Proposed modifications usefulness is demonstrated on benchmark test and real world problems. Genetic programming; uniform crossover; selective pressure recombination; self-configuration; symbolic regression; classification I. U.S. Government work not protected by U.S.
The technological inspection of the electrolyte composition in aluminum production is performed using calibration X-ray quantitative phase analysis (QPA). For this purpose, the use of QPA by the Rietveld method, which does not require the creation of multiphase reference samples and is able to take into account the actual structure of the phases in the samples, could be promising. However, its limitations are in its low automation and in the problem of setting the correct initial values of profile and structural parameters. A possible solution to this problem is the application of the genetic algorithm we proposed earlier for finding suitable initial parameter values individually for each sample. However, the genetic algorithm also needs tuning. A self-configuring genetic algorithm that does not require tuning and provides a fully automatic analysis of the electrolyte composition by the Rietveld method was proposed, and successful testing results were presented.
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