Improved Genetic Programming Algorithm Applied to Symbolic Regression and Software Reliability Modeling

Yongqiang Zhang,Huifang Cheng,Ruilan Yuan

doi:10.4236/jsea.2009.25047

Yongqiang Zhang, Huifang Cheng + Show 1 more

Open Access

https://doi.org/10.4236/jsea.2009.25047

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Abstract

The present study aims at improving the ability of the canonical genetic programming algorithm to solve problems, and describes an improved genetic programming (IGP). The proposed method can be described as follows: the first inves-tigates initializing population, the second investigates reproduction operator, the third investigates crossover operator, and the fourth investigates mutation operation. The IGP is examined in two domains and the results suggest that the IGP is more effective and more efficient than the canonical one applied in different domains.

Highlights

Genetic Programming (GP) is an automated method for creating a working computer program from a high-level problem statement of a problem [1,2]
The present study aims at improving the ability of the canonical genetic programming algorithm to solve problems, and describes an improved genetic programming (IGP)
The IGP is examined in two domains and the results suggest that the IGP is more effective and more efficient than the canonical one applied in different domains

Summary

Introduction

Genetic Programming (GP) is an automated method for creating a working computer program from a high-level problem statement of a problem [1,2]. It is a technique pioneered by John Koza [3] which enables computers to solve problems without being explicitly programmed and based on the idea of genetic algorithms presented by John Holland [4]. The goal is to use the concepts of Darwin evolution theory for computer program induction. In Genetic Programming solutions to a problem are represented as syntactic trees (or symbolic expressions), which are evolved in a population of programs towards an effective solution to specific problems according to Darwinism. The flexibility and expressiveness of computer program representation, combined with the powerful capability of evolutionary search, makes GP a promising method to solve a great variety of problems [6]

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