Genetic Programming with Historically Assessed Hardness

نویسندگان

  • Jon Klein
  • Lee Spector
چکیده

We present a variation of the genetic programming algorithm, called Historically Assessed Hardness (HAH), in which the fitness rewards for particular test cases are scaled in proportion to their relative difficulty as gauged by historical solution rates. The method is similar in many respects to some realizations of techniques such as implicit fitness sharing, stepwise adaptation of weights and fitness case selection, but the details differ and HAH is generally simpler and more efficient. It also leads to different generalizations. We present results from large-scale, systematic tests of HAH and we also discuss the technique in terms of the alternative explanations that it supports for the efficacy of implicit fitness sharing and related methods.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

DAMAGE AND PLASTICITY CONSTANTS OF CONVENTIONAL AND HIGH-STRENGTH CONCRETE PART II: STATISTICAL EQUATION DEVELOPMENT USING GENETIC PROGRAMMING

Several researchers have proved that the constitutive models of concrete based on combination of continuum damage and plasticity theories are able to reproduce the major aspects of concrete behavior. A problem of such damage-plasticity models is associated with the material constants which are needed to be determined before using the model. These constants are in fact the connectors of constitu...

متن کامل

Using of genetic programming in engineering

Intelligent systems are process coupled with robotics in industrial usually settings, though they may be used as diagnostic systems connected only to passive sensors. In this paper we use a new method which combines an intelligent genetic algorithm and multiple regression to predict the hardness of hardened specimens. The hardness of a material is an important mechanical property affecting mech...

متن کامل

Fitness Clouds and Problem Hardness in Genetic Programming

This paper presents an investigation of genetic programming fitness landscapes. We propose a new indicator of problem hardness for tree-based genetic programming, called negative slope coefficient, based on the concept of fitness cloud. The negative slope coefficient is a predictive measure, i.e. it can be calculated without prior knowledge of the global optima. The fitness cloud is generated v...

متن کامل

Bankruptcy Prediction: Dynamic Geometric Genetic Programming (DGGP) Approach

 In this paper, a new Dynamic Geometric Genetic Programming (DGGP) technique is applied to empirical analysis of financial ratios and bankruptcy prediction. Financial ratios are indeed desirable for prediction of corporate bankruptcy and identification of firms’ impending failure for investors, creditors, borrowing firms, and governments. By the time, several methods have been attempted in...

متن کامل

Negative Slope Coefficient: A Measure to Characterize Genetic Programming Fitness Landscapes

Negative slope coefficient has been recently introduced and empirically proven a suitable hardness indicator for some well known genetic programming benchmarks, such as the even parity problem, the binomial-3 and the artificial ant on the Santa Fe trail. Nevertheless, the original definition of this measure contains several limitations. This paper points out some of those limitations, presents ...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2008