A coevolutionary competitive learning environment for two antagonistic agents is presented. The agents are controlled by a new kind of computational network based on a compartmentalised model of neurons. We have taken the view that the genetic basis ...

In this paper we examine how the choice of functions in a genetic program (GP) affects the rate of code growth and the development of resilient individuals. We find that functions or combination of functions that produce the most resilient individuals ...

The effectiveness of content-based image retrieval (CBIR) systems can be improved by combining image features or by weighting image similarities, as computed from multiple feature vectors. However, feature combination do not make sense always and the ...

Developing artificial behaviours to control artificial creatures or vehicles is a task that can be employed by means of Evolutionary Algorithms. A game's artificial intelligence is usually developed by seasoned game developers, which need critical knowledge ...

Grammatical evolution (GE) is a form of grammar-based genetic programming. A particular feature of GE is that it adopts a distinction between the genotype and phenotype similar to that which exists in nature by using a grammar to map between the genotype ...

Search-based test case generation for object-oriented software is hindered by the size of the search space, which encompasses the arguments to the implicit and explicit parameters of the test object's public methods. The performance of this type of search ...

In this paper, statistical experimental design is used to characterize the transparent conducting oxide process of Ga-doped ZnO. Fractional factorial design with three center points are employed. In the process modeling, neural networks trained by the ...

We present two different single-core parallel SIMD linear genetic programming (LGP) systems for the IBM Cell Processor on the Playstation3. Our algorithms harness their computational power from the parallel capabilities of the Cell Processor. We implement ...

Previous work on introns and code growth in genetic programming is expanded on and tested experimentally. Explicitly defined introns are introduced to tree-based representations as an aid to measuring and evaluating intron behavior. Although it is shown ...

The 0/1 knapsack problem is one of the most exhaustively studied NP-hard combinatorial optimization problems. Many different approaches have been taken to obtain an approximate solution to the problem in polynomial time. Here we consider the biobjective ...

This project utilizes the evolutionary process found in Genetic Programming to evolve an improved decision formula for the Ant System algorithm. Two such improved formulae are discovered, one which uses the typical roulette wheel selection found in all ...

Particle Swarm Optimisation (PSO) uses a population of particles that fly over the fitness landscape in search of an optimal solution. The particles are controlled by forces that encourage each particle to fly back both towards the best point sampled ...

A study on the performance of solutions generated by Genetic Programming (GP) when the training set is relaxed (in order to allow for a wider definition of the desired solution) is presented. This performance is assessed through ...

We provide an overview of using genetic programming (GP) to model stock returns. Our models employ GP terminals (model decision variables) that are financial factors identified by experts. We describe the multi-stage training, testing and validation ...

Neutrality of some boolean parity fitness landscapes is investigated in this paper. Compared with some well known contributions on the same issue, we define some new measures that help characterizing neutral landscapes, we use a new sampling methodology, ...

In this paper, we describe a general-purpose, systematic algorithm, consisting of a genetic programming module and a k-nearest neighbor classifier to automatically create artificial features-features that are computer-crafted and may not have a known ...

This paper proposes a new grammar-guided genetic programming (GGGP) system by introducing two original genetic operators: crossover and mutation, which most influence the evolution process. The first, the so-called grammar-based crossover operator, strikes ...

A mechanism involving evolutionary genetic programming (GP) and the expectation maximization algorithm (EM) is proposed to generate feature functions, based on the primitive features, for an image pattern recognition system on the diagnosis of the disease ...

Currently, tools in the field of genetic programming are either geared towards solving certain type of problems, or are not easy to use (e.g., requiring actual source code modification of the software packages in order to generate a genetic programming ...

Earlier research into the examination timetabling problem focused on applying different methodologies to generate solutions to the problem. More recently research has been directed at developing hyper-heuristic systems for timetable construction. Hyper-heuristic ...