A graphical user interface for evolutionary algorithms

The purpose of Generic Evolutionary Algorithms Programming Library (GEA1) system is to provide researchers with an easy-to-use, widely applicable and extendable programming library which solves real-world optimization problems by means of evolutionary algorithms. It contains algorithms for various evolutionary methods, implemented genetic operators for the most common representation forms for individuals, various selection methods, and examples on how to use and expand the library. All these functions assure that GEA can be effectively applied on many problems. GraphGEA is a graphical user interface to GEA written with the GTK API. The numerous parameters of the evolutionary algorithm can be set in appropriate dialog boxes. The program also checks the correctness of the parameters and saving/restoring of parameter sets is also possible. The selected evolutionary algorithm can be executed interactively on the specified optimization problem through the graphical user interface of GraphGEA, and the results and behavior of the EA can be observed on several selected graphs and drawings. While the main purpose of GEA is solving optimization problems, that of GraphGEA is education and analysis. It can be of great help for students understanding the characteristics of evolutionary algorithms and researchers of the area can use it to analyze an EA's behavior on particular problems

Parallel Genetic Algorithms for the DAG Vertex Splitting Problem

Directed Acyclic Graphs are often used to model circuits and networks. The path length in such Directed Acyclic Graphs represents circuit or network delays. In the vertex splitting problem, the objective is to determine a minimum number of vertices from the graph to split such that the resulting graph has no path of length greater than a given δ. The problem has been proven to be NP-hard. A Sequential Genetic Algorithm has been developed to solve the DAG Vertex Splitting Problem. Unlike a standard Genetic Algorithm, this approach uses a variable chromosome length to represent the vertices that split the graph and a dynamic population size. Two String Length Reduction Methods to reduce the string length and two Stepping Methods to explore the search space have been developed. Combinations of these four methods have been studied and conclusions are drawn. A parallel version of the sequential Genetic Algorithm has been developed. It uses a fully distributed scheme to assign different string lengths to processors. A ring exchange method is used in order to exchange good individuals between processors. Almost linear speed-up and two cases of super linear speed-up are reported

Evolving a Computer Program to Generate Random Numbers Using the Genetic Programming Paradigm

This paper demonstrates that it is possible to genetically breed a computer program that is considered difficult to write, namely, a randomizer that converts a sequence of consecutive integers into pseudo-random bits with near maximal entropy. 1. INTRODUCTION AND OVERVIEW "How can computers learn to solve problems without being explicitly programmed?" This question, which is a central question in the fields of artificial intelligence and machine learning, can be approached using an analogy to the evolutionary process in nature. John Holland's pioneering 1975 Adaptation in Natural and Artificial Systems [3] described how the evolutionary process in nature can be applied to artificial systems using the "genetic algorithm" operating on fixed length character strings. Representation is a key issue in genetic algorithm work because genetic algorithms directly manipulate the coded representation of the problem and because the representation scheme can severely limit the window by which the..

A grammar-based technique for genetic search and optimization

The genetic algorithm (GA) is a robust search technique which has been theoretically and empirically proven to provide efficient search for a variety of problems. Due largely to the semantic and expressive limitations of adopting a bitstring representation, however, the traditional GA has not found wide acceptance in the Artificial Intelligence community. In addition, binary chromosones can unevenly weight genetic search, reduce the effectiveness of recombination operators, make it difficult to solve problems whose solution schemata are of high order and defining length, and hinder new schema discovery in cases where chromosome-wide changes are required.;The research presented in this dissertation describes a grammar-based approach to genetic algorithms. Under this new paradigm, all members of the population are strings produced by a problem-specific grammar. Since any structure which can be expressed in Backus-Naur Form can thus be manipulated by genetic operators, a grammar-based GA strategy provides a consistent methodology for handling any population structure expressible in terms of a context-free grammar.;In order to lend theoretical support to the development of the syntactic GA, the concept of a trace schema--a similarity template for matching the derivation traces of grammar-defined rules--was introduced. An analysis of the manner in which a grammar-based GA operates yielded a Trace Schema Theorem for rule processing, which states that above-average trace schemata containing relatively few non-terminal productions are sampled with increasing frequency by syntactic genetic search. Schemata thus serve as the building blocks in the construction of the complex rule structures manipulated by syntactic GAs.;As part of the research presented in this dissertation, the GEnetic Rule Discovery System (GERDS) implementation of the grammar-based GA was developed. A comparison between the performance of GERDS and the traditional GA showed that the class of problems solvable by a syntactic GA is a superset of the class solvable by its binary counterpart, and that the added expressiveness greatly facilitates the representation of GA problems. to strengthen that conclusion, several experiments encompassing diverse domains were performed with favorable results

Evolución de árboles en el problema del wall following robot

Este proyecto trata la hipótesis de que existe una relación entre forma arbórea de los individuos y su adaptación a las soluciones, permitiendo estimar el fitness de los individuos en función de su forma arbórea. Para ello se ha implementado el problema del wall following robot, según lo describió John R. Koza en su publicación “Evolution of Subsumption Using Genetic Programming”. Finalmente se comentarán las conclusiones extraídas a partir de los resultados obtenidos del motor de programación genética ProGen. Estos resultados constarán de una comparación de rendimiento entre la programación genética en árbol y la programación genética tradicional, evaluando tanto los resultados brutos, como el coste empleado para extraerlos. Por último se implementó una herramienta de visualización de resultados, que permite poder interpretar de forma intuitiva los experimentos. El movimiento de los robots se representa como una línea azul dentro de la habitación de paredes rojas como se muestra en las distintas capturas de pantalla de este documento. _________________________________________________________________________________________________________________________This Project deals with the hypothesis that there is a relationship between the individual’s tree form and their way to find a solution, allowing us to estimate the fitness value of the individuals based on their tree form. In order to fulfill the hypothesis this Project has developed the Wall Fallowing Robot problem as described by John R. Koza in his paper “Evolution of Subsumption Using Genetic Programming”. The ProGen genetic programming engine was used to achieve this and obtain the results discussed in the conclusions chapter of this memo. Finally, a viewer was developed to be able to easily show the results of each experiment. In this viewer the path followed by the robots is represented by a blue line inside of a red square room. Screen captures of this viewer have been included in this document.Ingeniería en Informátic