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GLOBAL OPTIMIZATION, SEARCH AND MACHINE LEARNING METHOD BASED ON THE LAMARCKIAN PRINCIPLE OF INHERITANCE OF ACQUIRED CHARACTERISTICS
GLOBAL OPTIMIZATION, SEARCH AND MACHINE LEARNING METHOD BASED ON THE LAMARCKIAN PRINCIPLE OF INHERITANCE OF ACQUIRED CHARACTERISTICS
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机译:基于获得性特征遗传的拉马克原理的全局优化,搜索和机器学习方法
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摘要
The invention discloses a global optimization, search and machine learning method based on the Lamarckian principle of inheritance of acquired characteristics, comprising step 1: constructing an objective function ƒ(P) according to the problem being solved, where P represents a set of candidate solutions to the problem; step 2: encoding P into a genetic algorithm (GA) chromosome, inputting or automatically calculating algorithmic parameters of the GA, and initializing the algorithm and the population of candidate solution generation G0={P01, P02, . . . , P0S}, where S is the size of the population G and 0 stands for the initial generation; step 3: at generation k, optimizing the prevailing population of the candidate solutions Gk={Pk1, Pk2, . . . , PkS} iteratively using a Lamarckian “Heredity Operator” and a “Use-and-Disuse Operator” based on the values of ƒ(Gk); and step 4: outputting the final set of optimal solutions to the problem.
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机译:本发明公开了一种基于拉马克遗传继承特征的全局优化,搜索和机器学习的方法,包括步骤1:根据要解决的问题构造目标函数ƒ(P),其中P表示候选解集解决问题步骤2:将P编码到遗传算法(GA)染色体中,输入或自动计算GA的算法参数,并初始化算法和候选解生成量G 0 Sub> = {P 0 Sub> 1 Sup>,P 0 Sub> 2 Sup> ,。 。 。 ,P 0 Sub> S Sup>},其中S是总体G的大小,0代表初始代;步骤3:在第k代,优化候选解决方案的主要种群G k Sub> = {P k Sub> 1 Sup>,P k < / Sub> 2 Sup>,。 。 。 ,P k Sub> S Sup>}根据ƒ(G k <的值,使用Lamarckian的“遗传运算符”和“使用和废弃运算符”进行迭代/ Sub>);步骤4:输出针对该问题的最终最优解集。
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