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GEP（基因表达式程序设计）是一种新的演化算法，该文件是基本的GEP编程，采用功能的封装格式，测试通过，能够方便的使用。-GEP (Gene Expression Program Design) is a new evolutionary algorithm, which is the basic GEP programming, functional package format, through testing, to facilitate their use.
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JGAP是一种遗传算法和遗传规划的组成部分提供了一个Java框架。它提供了基本的遗传机制，可以很容易地用来申请进化原则问题的解决办法。见实例演示或观赏树的图形，可以创建JGAP找到解决办法的基因进化程序。-JGAP is a genetic algorithm and genetic programming an integral part of providing a Java framework. It provides basic genetic mechanisms, can easil
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研究基于遗传编程的可持续性进化算法的理论与方法，并建立了该算法的数学模型及运算流程.HFC算法实现源代码.-Research based on genetic programming evolutionary algorithm for the sustainability of the theory and method, and set up a mathematical model of the algorithm and the computation flow. HFC algorit
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FEP快速进化规划算法，参见姚新的文章《Evolutionary Programming Made Faster》-FEP fast evolutionary programming algorithm, see Yao xin s paper《Evolutionary Programming Made Faster》
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Apache Mahout 是 Apache Software Foundation（ASF）旗下的一个开源项目，提供一些可扩展的机器学习领域经典算法的实现，旨在帮助开发人员更加方便快捷地创建智能应用程序。经典算法包括聚类、分类、协同过滤、进化编程等-Apache Mahout is the Apache Software Foundation (ASF)' s an open source project, to provide some areas of expansion of th
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TinyGP是一个用java实现的遗传编程程序，它实现了遗传编程基本过程-TinyGP is a highly optimised GP system that was originally developed by Riccardo Poli to meet the specifications set out in the TinyGP competition of the Genetic and Evolutionary Computation Conference (GECCO) 200
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基于高斯变异的进化规划用于解决非线性无约束函数问题-The Evolutionary programming for sloving the nonlinear unconstrained function by adapting the Gauss mutation
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本书论述深入浅出，从简单的实力介绍没中算法的原理，然后深入讨论他们的基本理论及应用技术，图文并茂，便于自学；包括，遗传算法，遗传规则，进化策略，金花规则4中算法，本书分别介绍他们的基本知识，基本理论及实施技术，全书共分5涨，跟别为绪论，遗传算法，遗传规划，进化策略，进化规划-This book discusses in simple terms, the strength of introduction from a simple principle of the algorithm is no
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onventional optimization algorithms using linear and non-linear programming sometimes have difficulty in finding the global optima or in case of multi-objective optimization, the pareto front. A lot of research has now been directed towards evolution
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A flexible programming library for evolutionary computation. Steady-state, generational and island model genetic algorithms are supported, using Darwinian, Lamarckian or Baldwinian evolution. Includes support for multiprocessor and distributed system
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Describes multiple crossover operators used in evolutionary programming.
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该资料讲述的进化算法的相关概念，发展历史，以及相关编程示例。(The related concepts, history, and related programming examples of the evolutionary algorithms are described.)
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