A measure of search difficulty, fitness distance correlation (FDC), is introduced and examined in relation to genetic algorithm (GA) performance. In many cases, this correlation can be used to predict the performance of a GA on problems with known global maxima. It correctly classifies easy deceptive problems as easy and difficult non-deceptive problems as difficult, indicates when Gray coding will prove better than binary coding, and is consistent with the surprises encountered when GAs were used on the Tanese and royal road functions. The FDC measure is a consequence of an investigation into the connection between GAs and heuristic search. 1 INTRODUCTION A correspondence between evolutionary algorithms and heuristic state space search is developed in (Jones, 1995b). This is based on a model of fitness landscapes as directed, labeled graphs that are closely related to the state spaces employed in heuristic search. We examine one aspect of this correspondence, the relationship between...

We apply linear genetic programming to several diagnosis problems in medicine. An efficient algorithm is presented that eliminates intron code in linear genetic programs. This results in a significant speedup which is especially interesting when operating with complex datasets as they are occuring in real-world applications like medicine. We compare our results to those obtained with neural networks and argue that genetic programming is able to show similar performance in classification and generalization even within a relatively small number of generations.

The problem of evolving an artificial ant to follow the Santa Fe trail is used to study the well known genetic programming feature of growth in solution length. Known variously as "bloat", "fluff" and increasing "structural complexity", this is often described in terms of increasing "redundancy" in the code caused by "introns". Comparison between runs with and without fitness selection pressure, backed by Price's Theorem, shows the tendency for solutions to grow in size is caused by fitness based selection. We argue that such growth is inherent in using a fixed evaluation function with a discrete but variable length representation. With simple static evaluation search converges to mainly finding trial solutions with the same fitness as existing trial solutions. In general variable length allows many more long representations of a given solution than short ones. Thus in search (without a length bias) we expect longer representations to occur more often and so representation length to te...

One serious problem of standard Genetic Programming (GP) is that evolved structures appear to drift towards large and slow forms on average. This paper presents a novel analysis of the role played by variable complexity in the selection and survival of GP expressions. It defines a particular property of GP representations, called rooted tree-schema, that sheds light on the role of variable complexity of evolved structures. A rooted tree-schema is a relation on the space of tree-shaped structures which provides a quantifiable partitioning of the search space. The paper analyzes the influence of parsimony pressure on selection and growth of structures. Experimental evidence confirms theoretical predictions. 1 Introduction Genetic programming (GP) uses open-ended complexity representations of flexible semantics (Koza1992). GP evolves a population of expressions in some problem dependent language that encode problem solutions. Evolved expressions are tree structured and can be interpret...

The majority of commercial computers today are register machines of von Neumann type. We have developed a method to evolve Turing-complete programs for a register machine. The described implementation enables the use of most program constructs, such as arithmetic operators, large indexed memory, automatic decomposition into subfunctions and subroutines (ADFs), conditional constructs i.e. if-then-else, jumps, loop structures, recursion, protected functions, string and list functions. Any C-function can be compiled and linked into the function set of the system. The use of register machine language allows us to work at the lowest level of binary machine code without any interpreting steps. In a von Neumann machine, programs and data reside in the same memory and the genetic operators can thus directly manipulate the binary machine code in memory. The genetic operators themselves are written in C-language but they modify individuals in binary representation. The result is an execution spe...

Genetic Programming (GP) uses variable size representations as programs. Size becomes an important and interesting emergent property of the structures evolved by GP. The size of programs can be both a controlling and a controlled factor in GP search. Size influences the efficiency of the search process and is related to the generality of solutions. This paper analyzes the size and generality issues in standard GP and GP using subroutines and addresses the question whether such an analysis can help control the search process. We relate the size, generalization and modularity issues for programs evolved to control an agent in a dynamic and non-deterministic environment, as exemplified by the Pac-Man game. 1 Introduction Genetic Programming (Koza, 1992) has been applied to a variety of machine learning applications formulated mostly as classification or prediction problems. Some examples include the prediction of omega loops in proteins and the transmembrane problem, symbolic regression...

Evolutionary computation (EC) consists of the design and analysis of probabilistic algorithms inspired by the principles of natural selection and variation. Genetic Programming (GP) is one subfield of EC that emphasizes desirable features such as the use of procedural representations, the capability to discover and exploit intrinsic characteristics of the application domain, and the flexibility to adapt the shape and complexity of learned models. Approaches that learn monolithic representations are considerably less likely to be effective for complex problems, and standard GP is no exception. The main goal of this dissertation is to extend GP capabilities with automatic mechanisms to cope with problems of increasing complexity. Humans succeed here by skillfully using hierarchical decomposition and abstraction mechanisms. The translation of such mechanisms into a general computer implementation is a tremendous challenge, which requires a firm understanding of the interplay between repr...

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One serious problem of standard Genetic Programming (GP) is that evolved expressions appear to drift towards large and slow forms on average. This report presents a novel analysis of the role played by variable complexity in the selection and survival of GP expressions. It defines a particular property of GP representations, called rooted tree-schema, that sheds light on the role of variable complexity of evolved representations. A tree-schema is a relation on the space of tree-shaped structures which provides a quantifiable partitioning of the search space. The present analysis answers questions such as: What role does variable complexity play in the selection and survival of evolved expressions? What is the influence of a parsimony penalty? How heavy should parsimony penalty be weighted or how should it be adapted in order to preserve the underlying optimization process? Are there alternative approaches to simulating a parsimony penalty that do not result in a change of the fitness l...