# Genetic Programming Theory and Practice II (Genetic Programming, Volume 8)

Language: English

Pages: 330

ISBN: 1441935894

Format: PDF / Kindle (mobi) / ePub

This volume explores the emerging interaction between theory and practice in the cutting-edge, machine learning method of Genetic Programming (GP). The contributions developed from a second workshop at the University of Michigan's Center for the Study of Complex Systems where leading international genetic programming theorists from major universities and active practitioners from leading industries and businesses met to examine how GP theory informs practice and how GP practice impacts GP theory. Chapters include such topics as financial trading rules, industrial statistical model building, population sizing, the roles of structure in problem solving by computer, stock picking, automated design of industrial-strength analog circuits, topological synthesis of robust systems, algorithmic chemistry, supply chain reordering policies, post docking filtering, an evolved antenna for a NASA mission and incident detection on highways.

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Underneath a specified function node. Therefore, this module mechanism can be use to incorporate domain knowledge to design GP tree structure. In this work, we want GP to combine multiple technical indicators. To achieve that, we first add Boolean function combinators AND, OR, NAND, NOR to the function set. Additionally, we specify some of the combinators as higher-order functions. In this way, technical indicators can be evolved inside X modules, which are then integrated together by the.

Referred to as the GP model. The transformations given in Table 3-2 were then applied to the data as indicated by the functional form of the derived GP function. Table 3-2. Variable transformations suggested by GP model. I Original Variable I Transformed Variable I The transformed variables were used to fit a second order linear regression model shown in equation (I). The resulting model, referred to as the Transformed Linear Model (TLM), had an Ii' of 0.88, no evidence of significant Lack.

Function of Parameter pvalues that correspond to easier settings tuning parameter P for have higher percentages of successful trials. two different population sizes for both tournament and fitness-proportionate selection. Each data point is the successful-trials ratio for 1,000 trials. A total of 82,000 trials is depicted. What is striking about the results shown is that GP is only able to assemble a fraction of the total number of possible fragments into a single individual. For population sizes.

Residual stockpicking performance as well as other more esoteric elements (volatility, market cap size, labor intensity, etc.). The net result of this analysis is a series of statistics that are suggestive of areas in which we do well and poorly. Often these statistics are quite time-period specific and require Genetic Programming in a Stock Picking Context additional insight (or intuition) that is generally well beyond the degrees of freedom permitted by the data. One area that needed.

To GP (and all of evolutionary computation) he has shown that providing the rewards for building blocks is necessary for compiex adaptation. He has observed considerable non-monotonicity in the mutational dynamics of populations that evolve EQU. Neutral and deleterious mutations occur. Sometimes a trade-off occurs - the final mutation leading to EQU will result in a simpler function being eliminated. Each population that evolved EQU did so by a different path and arrived at a different solution.