Automatically Defined Functions Research Articles

Machine Learning Approach Based on Gene Expression Programming for Dynamic Production Scheduling Problem

Dynamic Job shop scheduling problem (DJSSP) which involves the dynamic characteristic of job arriving over time is one of the most popular scheduling problems in manufacturing system. In the paper, a machine learning approach is proposed for DJSSP. The proposed machine learning approach employs gene expression programming (GEP) which introduces automatically defined functions (ADF) into the representation of chromosome to construct efficient scheduling rules (SRs) automatically for DJSSP. The proposed GEP-based machine learning approach is evaluated and the results show that the approach is effective.

Coevolving functions in genetic programming

In this paper we introduce a new approach to the use of automatically defined functions (ADFs) within genetic programming. The technique consists of evolving a number of separate sub-populations of functions which can be used by a population of evolving main programs. We present and refine a set of mechanisms by which the number and constitution of the function sub-populations can be defined and compare their performance on two well-known classification tasks. A final version of the general approach, for use explicitly on classification tasks, is then presented. It is shown that in all cases the coevolutionary approach performs better than traditional genetic programming with and without ADFs.

Evolutionary program induction directed by logic grammars

Program induction generates a computer program that can produce the desired behavior for a given set of situations. Two of the approaches in program induction are inductive logic programming (ILP) and genetic programming (GP). Since their formalisms are so different, these two approaches cannot be integrated easily, although they share many common goals and functionalities. A unification will greatly enhance their problem-solving power. Moreover, they are restricted in the computer languages in which programs can be induced. In this paper, we present a flexible system called LOGENPRO (The LOgic gramar-based GENetic PROgramming system) that uses some of the techniques of GP and ILP. It is based on a formalism of logic grammars. The system applies logic grammars to control the evolution of programs in various programming languages and represent context-sensitive information and domain-dependent knowledge. Experiments have been performed to demonstrate that LOGENPRO can emulate GP and GP with automatically defined functions (ADFs). Moreover, LOGENPRO can employ knowledge such as argument types in a unified framework. The experiments show that LOGENPRO has superior performance to that of GP and GP with ADFs when more domain-dependent knowledge is available. We have applied LOGENPRO to evolve general recursive functions for the even-n-parity problem from noisy training examples. A number of experiments have been performed to determine the impact of domain-specific knowledge and noise in training examples on the speed of learning.

Book review: Genetic Programming I1: Automatic Discovery of Reusable Programs by John R. Koza (The MIT Press 1994)

This book is a followon to the book in which John Koza introduced genetic programming (GP) to the world "enetic Programming: On the Programming of Computers by Means of Natural Selection" [5] 1 . As such, the primary intended audience is someone already familiar with GP; however, Koza does provide introductory material to both genetic algorithms (GA) and GP. The driving force behind this book is a method to automatically decompose a program into solvable components. The book presents this method, called automatically defined functions (ADF), and then presents case studies of the application of this method to a variety of problems. While this book's size is intimidating, there is a wealth of information to be found by the reader willing to conduct a prolonged campaign. The reader is advised to study the first seven chapters of the book to gain an understanding of the concepts behind ADFs. Then the reader should be able to select which case studies he or she finds to be of interest. If the reader is feeling overwhelmed by the information presented in the book, there are several concise chapters dealing with ADFs by Koza and others in Advances in Genetic Programming edited by Kenneth Kinnear [3] 2 .

Genetic Programming II: Automatic Discovery of Reusable Programs.

Reading Genetic Programming IE Automatic Discovery ofReusable Programs (GPII) in its entirety is not a task for the weak-willed because the book without appendices is about 650 pages. entire previous book by the same author [1] is devoted to describing Genetic Programming (GP), while this book is a sequel extolling an extension called Automatically Defined Functions (ADFs). The author, John R. Koza, argues that ADFs can be used in conjunction with to improve its efficacy on large problems. An automatically defined function (ADF) is a function (i.e., subroutine, procedure, module) that is dynamically during a run of genetic programming and which may be called by a calling program (e.g., a main program) that is simultaneously being evolved (p. 1). Dr. Koza recommends adding the ADF technique to the GP toolkit. The book presents evidence that it is possible to interpret with ADFs as performing either a top-down process of problem decomposition or a bottom-up process of representational change to exploit identified regularities. This is stated as Main Point 1. Main Point 2 states that ADFs work by exploiting inherent regularities, symmetries, patterns, modularities, and homogeneities within a problem, though perhaps in ways that are very different from the style of programmers. Main Points 3 to 7 are appropriately qualified statements to the effect that, with a variety of problems, ADFs pay off be-