Using tabu search and genetic algorithms in mathematics research

This paper discusses an ongoing project which uses computational heuristic search techniques such as tabu search and genetic algorithms as a tool for mathematics research. We discuss three ways in which such search techniques can be useful for mathematicians: in nding counterexamples to conjectures, in enumerating examples, and in nding sequences of transformations between two objects which are conjectured to be related. These problem-types are discussed using examples from topology

Is it Time for Computational Creativity to Grow Up and Start being Irresponsible?

A recent definition of computational creativity has em- phasised that computational creativity systems should “take on certain responsibilities” for generating creative behaviour. This paper examines the notion of responsibilities in that definition, and looks at a number of aspects of the creative act and its context that might play a role in that responsibility, with an emphasis on artistic and musical creativity. This problematises the seemingly simple distinction between systems that have responsibilities for creative activity and those which support or provide tools for creativity. The paper con- cludes with a discussion of an alternative approach to the subject, which argues that the responsibility for creative action is typically diffused through a complex human/computer system, and that a “systems thinking” approach to locating computational creativity might ask better questions than one that tries to pin creative responsibility to a particular agent

A Genetic Programming Problem Definition Language Code Generator for the EpochX Framework

There are many different genetic programming (GP) frameworks that can be used to implement algorithms to solve a particular optimization problem. In order to use a framework, users need to become familiar with a large numbers of source code before actually implementing the algorithm, adding a learning overhead. In some cases, this can prevent users from trying out different frameworks. This paper discusses the implementation of a code generator in the EpochX framework to facilitate the implementation of GP algorithms. The code generator is based on the GP defini- tion language (GPDL), which is a framework-independent language that can be used to specify GP problems

A new sequential covering strategy for inducing classification rules with ant colony algorithms

Ant colony optimization (ACO) algorithms have been successfully applied to discover a list of classification rules. In general, these algorithms follow a sequential covering strategy, where a single rule is discovered at each iteration of the algorithm in order to build a list of rules. The sequential covering strategy has the drawback of not coping with the problem of rule interaction, i.e., the outcome of a rule affects the rules that can be discovered subsequently since the search space is modified due to the removal of examples covered by previous rules. This paper proposes a new sequential covering strategy for ACO classification algorithms to mitigate the problem of rule interaction, where the order of the rules is implicitly encoded as pheromone values and the search is guided by the quality of a candidate list of rules. Our experiments using 18 publicly available data sets show that the predictive accuracy obtained by a new ACO classification algorithm implementing the proposed sequential covering strategy is statistically significantly higher than the predictive accuracy of state-of-the-art rule induction classification algorithms

Event-based graphical monitoring in the EpochX genetic programming framework

EpochX is a genetic programming framework with provision for event management – similar to the Java event model – allowing the notification of particular actions during the lifecycle of the evolutionary algorithm. It also provides a flexible Stats system to gather statistics measures. This paper introduces a graphical interface to the EpochX genetic programming framework, taking full advantage of EpochX's event management. A set of representation-independent and tree-dependent GUI components are presented, showing how statistic information can be presented in a rich format using the information provided by EpochX's Stats system

Evolving Recursive Programs using Non-recursive Scaffolding

Genetic programming has proven capable of evolving solutions to a wide variety of problems. However, the successes have largely been with programs without iteration or recursion; evolving recursive programs has turned out to be particularly challenging. The main obstacle to evolving recursive programs seems to be that they are particularly fragile to the application of search operators: a small change in a correct recursive program generally produces a completely wrong program. In this paper, we present a simple and general method that allows us to pass back and forth from a recursive program to an associated non-recursive program. Finding a recursive program can be reduced to evolving non-recursive programs followed by converting the optimum non-recursive program found to the associated optimum recursive program. This avoids the fragility problem above, as evolution does not search the space of recursive programs. We present promising experimental results on a test-bed of recursive problems

Detecting collisions in sets of moving particles: a survey and some experiments

Detecting and responding to collisions between particles is an important requirement for building simulations in computational science. Due to the large number of potential collisions it is impractical to check all possibilities, so the development of algorithms which narrow down the number of possible searches to a small number is important. In this paper we review various algorithms for this task, and give results from a number of experiments which demonstrate the relative efficiency of these algorithms on a fundamental problem of detecting collisions between particles undergoing Brownian motion. The general slant of the paper is towards the development of algorithms for simulating microbiological systems

Improving the predictive performance of SAFEL: A Situation-Aware FEar Learning model

In this paper, we optimize the predictive performance of a Situation-Aware FEar Learning model (SAFEL) by investigating the relationship between its parameters. SAFEL is a hybrid computational model based on the fear-learning system of the brain, which was developed to provide robots with the capability to predict threatening or undesirable situations based on temporal context. The main aim of this work is to improve SAFEL's emotional response. An emotional response coherent with environmental changes is essential not only for self-preservation and adaptation purposes, but also for improving the believability and interaction skills of companion robots. Experiments with a NAO humanoid robot show that adjusting the ratio between two parameters of SAFEL can significantly increase the predictive performance and reduce parameter settings

Artistic and Musical Applications of Internet Search Technologies: Prospects and a Case Study

This paper explores the idea of internet search as a technology to underpin artistic creation. Concepts of interactivity in art and music are explored, and then an overview of different types of internet-based art is presented. A number of different ways in which internet search have the potential to underpin artistic and musical activity are then discussed, with ideas such as the idea of a collective readymade and aesthetics of mass and unexpected connections are used to give this discussion a theoretical basis. Finally, a case study is given, in which the author discusses one of their own multimedia artworks that makes substantial use of internet search