A Global Representation Scheme for Genetic Algorithms

. Modelling the behaviour of genetic algorithms has concentrated on Markov chain analysis. However, Markov chains yield little insight into the dynamics of the underlying mechanics and processes. Thus, a framework and methodology for global modelling and visualisation of genetic algorithms is described, using tools from the field of Information Theory. Using Principal Component Analysis (PCA) based on the Karhunen-Lo`eve transform, a generation (instance of a population) is transformed into a compact low dimensional eigenspace representation. A pattern vector (set of weights) is calculated for each population of strings, by projecting it into the eigenspace. A 3D manifold or global signature is derived from the set of computed pattern vectors. Principal Components Analysis is applied to a GA parameterised by three encoding schemes - binary, E-code and Gray - and a test platform consisting of twelve functions. The resultant manifolds are described and correlated. The paper is concluded ..

Pursuit-evasion using evolutionary algorithms in an immnersive three-dimensional environment

In view of the biological prevalence of pursuit-evasion conies6 they provide a useful test-bed for research into novel bio-inspired computing and control systems. In this paper we investigate the evolution of pursuit-evasion strategies in a virtual-reality environment created using the Unreal World Editor. The Unreal World Editor (UnrealED), original& designed for use with thep opular 30 game Unreal, is an easily available editor which can be used for the creation and modification of a wide variety of immersive environments In this paper we model buildings on the University of Limerick campus, and their associated exteriors This paper makes use of an extension tot he original Unreal game engine called a mutator, more specifically the Gamebots mutator designed and released by the University of Southern California’s Information Sciences Institute. This atension allows characters in the game to be controlled via network sockets connected to other programs. The game feeds sensory information to the character over the network connection. Based on this information, the client program can decide what actions the being should take and issues commands back over the network to the game, in order to control the actions of the entie. The client program incorporates a genetic algorithm to control the two individuals involved

Genocodes for Genetic Algorithms

The issue of which encoding scheme to use for the genetic algorithm (GA) genocode, has not received its due recognition by the GA community. This is based on the premise that the GA representation debate has been concluded as a consequence of the schemata theorem, which states that low cardinality alphabets are a prerequisite. However, in contrast to the theory, practitioners continually report the success of real genocodes. A brief review of the theory of representation in genetic algorithms is provided. Six genocodes are evaluated, straight binary, Gray, E-code, Real-step (step mutation with fine granularity), Real-step-round (step mutation with coarse granularity) and Real-stochastic (stochastic mutation). The results support the concept of a dynamic adaptive genocode. 1 Introduction The choice of which encoding scheme to use as the genocode for problem representation is a significant factor in the application of genetic algorithms (GAs) [18]. Maximising the correlation between the..

A GA-inspired approach to the reduction of edge crossings in force-directed layouts

We report on our findings using a genetic algorithm (GA) as a preprocessing step for force-directed graph drawings to find a smart initial vertex layout (instead of a random initial layout) to decrease the number of edge crossings in the graph. We demonstrate that the initial layouts found by our GA improve the chances of finding better results in terms of the number of edge crossings, especially for sparse graphs and star-shaped graphs. In particular we demonstrate a reduction in edge-crossings for the class of star-shaped graphs by using our GA over random vertex placement in the order of 3:1