Peter John Bentley

Learn More
This paper continues a theme of exploring algorithms based on principles of biological development for tasks such as pattern generation, machine learning and robot control. Previous work has investigated the use of genes expressed as fractal proteins to enable greater evolvability of gene regulatory networks (GRNs). Here, the evolution of such GRNs is(More)
This paper investigates one of the newest and most exciting methods in computer science to date: employing computers as creative problem solvers by using evolution to explore for new solutions. The paper introduces and discusses the new understanding that explorative evolution relies upon a representation based on components rather than a parameterisation(More)
This paper describes an attempt to enable computers to generate truly novel conceptual designs of three dimensional solid objects by using genetic algorithms (GAs). These designs are represented using spatial partitions of 'stretched cubes' with optional intersecting planes [1]. Each individual three-dimensional solid object has its functionality specified(More)
The Ant Colony Metaheuristic was originally proposed for tackling optimization problems. More recent research has suggested that it can be applied for automatic generation of programs. By allowing the artificial ants to visit functions and terminals nodes, they become able to build pheromone trails that represent computer programs for optimizing a fitness(More)
This paper focuses on the problem of how to rank a population of solutions into order of fitness within a genetic algorithm for multiobjective optimization applications. Attention is paid to the fact that the set of acceptable solutions to a problem is usually only a small subset of all Pareto-optimal solutions to the problem. Two key concepts essential to(More)