Evolving the user interface

A method is presented for evolving Graphical User Interfaces using Genetic Algorithms. The fitness evaluation is based on a series of constraints, which must be met by the user interface. Examples are used to demonstrate the use of positional, style and functionality constraints and the final example shows the evolution of a complete (although simple) software application

An improved representation for evolving programs

A representation has been developed that addresses some of the issues with other Genetic Program representations while maintaining their advantages. This combines the easy reproduction of the linear representation with the inherita- ble characteristics of the tree representation by using fixed-length blocks of genes representing single program statements. This means that each block of genes will always map to the same statement in the parent and child unless it is mutated, irrespective of changes to the surrounding blocks. This method is compared to the variable length gene blocks used by other representations with a clear improvement in the similarity between parent and child. In addition, a set of list evaluation and manipulation functions was evolved as an application of the new Genetic Program components. These functions have the common feature that they all need to be 100% correct to be useful. Traditional Genetic Programming problems have mainly been optimization or approximation problems. The list results are good but do highlight the problem of scalability in that more complex functions lead to a dramatic increase in the required evolution time

Comparing content-filter techniques for stopping spam

There are many new theoretical techniques for detecting spam e-mail based upon the message contents. Although Bayesian methods are the most wellknown, there are other approaches for classifying information. This paper establishes some criteria for measuring spam filter effectiveness and compares the Boosting and Support Vector Machine approaches with some well-known existing filter software. It also examines ways of transforming e-mail messages into a form which is more readily processable by such algorithms

Evolving readable Perl

A program is informally deemed readable, for the purpose of this experiment, if it is easy for a person to follow the steps that the program takes to solve the problem. In this experiment, readability is achieved by constraining the available syntax for generating solutions. The Genetic Programming (GP) system created uses the target language Perl because it is an interpreted, untyped, robust procedural language which has good error handling and recovery

Evolving Perl

A list of requirements for a genetic programming representation is put forward and a representation separating the genotype and phenotype with a linear genome is presented. The target language for the genetic program is Perl. The mapping process, between the genotype and phenotype, converts blocks of four genes into program statements. This process is context-free and therefore provides inheritable characteristics. The representation is tested by evolving a selection of list evaluation and manipulation functions which are all evolved from the same language subset, with good results

Packet transmission optimisation using Genetic Algorithms

A Genetic Algorithm (ga) is used to optimise the parameters for a sequence of packets sent over the Internet. Only the parameters that a client machine can change are used and the fitness is based on the delay time returned by the Traceroute program. The ga performance is compared to a fixed packet size with no priority used to assess the status of the network. The ga generally performed to the same level as the control settings but in some cases significant improvements were made

Honey Plotter and the Web of Terror

Honeypots are a useful tool for discovering the distribution of malicious traffic on the Internet and how that traffic evolves over time. In addition, they allow an insight into new attacks appearing. One major problem is analysing the large amounts of data generated by such honeypots and correlating between multiple honeypots. Honey Plotter is a web-based query and visualisation tool to allow investigation into data gathered by a distributed honeypot network. It is built on top of a relational database, which allows great flexibility in the questions that can be asked and has automatic generation of visualisations based on the results of queries. The main focus is on aggregate statistics but individual attacks can also be analysed. Statistical comparison of distributions is also provided to assist with detecting anomalies in the data; helping separate out common malicious traffic from new threats and trends. Two short case studies are presented to give an example of the types of analysis that can be performed

Automating rolling stock diagramming and platform allocation

Rolling stock allocation is the process of assigning timetable schedules to physical train units. This is primarily done by connecting together schedules at their terminal locations (known as schedule associations). Platforming allocation is the process of assigning those associations to particular platforms. A simple last-in, first-legal-out algorithm is used for rolling stock allocation that performs comparably to the traditional manual approach but only takes a few seconds as opposed to days or weeks in many manual cases. A simple stochastic hill-climbing approach is used for assigning associations to platforms to provide a conflict-free platform allocation within a few seconds. These two approaches are tested on real train planning problems with excellent results that would allow an expert to rapidly produce optimal or near optimal solutions. The time saving using these approaches can be used by the train planner to try out various options or have greater checking of robustness of the solutions created

Train timetable generation using genetic algorithms

The scheduling of railway trains has been a research problem for many years. Many of the choices required are not known a priori and require exploration of the problem to determine them. A modular Genetic system was designedmake the evaluation function and preparation of the timetable tractable. The Genetic system consists of a Genome, split into Chromosomes so the extra choices that become known throughout the evolution can be added to the Chromosomes. A weighted fitness function and a multiobjective non-dominated fitness function were tried, and then partial objective ranking was added. The system has tackled a mixture of problems has produced promising results

Allocating railway platforms using a genetic algorithm

This paper describes an approach to automating railway station platform allocation. The system uses a Genetic Algorithm (GA) to find how a station’s resources should be allocated. Real data is used which needs to be transformed to be suitable for the automated system. Successful or ‘fit’ allocations provide a solution that meets the needs of the station schedule including platform re-occupation and various other constraints. The system associates the train data to derive the station requirements. The Genetic Algorithm is used to derive platform allocations. Finally, the system may be extended to take into account how further parameters that are external to the station have an effect on how an allocation should be applied. The system successfully allocates around 1000 trains to platforms in around 30 seconds requiring a genome of around 1000 genes to achieve this