ABSTRACT

The solution of the problem of building nonlinear models (mathematical expressions, functions, algorithms, and programs) based on an experimental data set, a set of variables, or a set of basic functions and operations is considered. A metaheuristic programming method for the evolutionary synthesis of nonlinear models has been developed; in this method, a chromosome is represented in the form of a vector of real numbers and allows the use of various bio-inspired (nature-inspired) optimization algorithms in the search for models. The efficiency of the proposed algorithm is estimated using ten bio-inspired algorithms (two modifications of the genetic programming algorithm, differential evolution algorithm, particle swarm optimization algorithm, artificial bee colony algorithm, teaching-learning-based optimization algorithm and two of its modifications, covariance matrix adaptation evolution strategy, and simultaneous heat transfer search algorithm) and compared with the results of standard algorithms of genetic programming, grammatical evolution, and Cartesian genetic programming. The experiments have shown a significant advantage of this approach as compared to the above-mentioned algorithms in terms of both the time needed for finding the solution (greater than by an order of magnitude in most cases) and the probability of finding a given function (model) (greater than twice in many cases).

中文翻译：

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非线性模型综合元启发式编程方法的发展

摘要

考虑了基于实验数据集，变量集或基本功能和操作集构建非线性模型（数学表达式，函数，算法和程序）的问题的解决方案。开发了一种用于非线性模型演化综合的元启发式程序设计方法；在这种方法中，染色体以实数向量的形式表示，并允许在搜索模型时使用各种受生物启发（自然启发）的优化算法。使用十种生物启发算法（遗传编程算法的两种改进，差分进化算法，粒子群优化算法，人工蜂群算法，基于教学的优化算法及其两个改进，即协方差矩阵适应进化策略和同时传热搜索算法），并与标准遗传算法，语法进化算法和笛卡尔遗传算法的结果进行了比较。实验表明，与上述算法相比，该方法具有显着的优势，即找到解决方案所需的时间（大多数情况下大于一个数量级）和找到给定函数的概率（型号）（在很多情况下大于两倍）。和笛卡尔遗传编程。实验表明，与上述算法相比，该方法具有显着的优势，即找到解决方案所需的时间（大多数情况下大于一个数量级）和找到给定函数的概率（型号）（在很多情况下大于两倍）。和笛卡尔遗传编程。实验表明，与上述算法相比，该方法具有显着的优势，即找到解决方案所需的时间（大多数情况下大于一个数量级）和找到给定函数的概率（型号）（在很多情况下大于两倍）。