. This paper presents a new form of Genetic Programming called Cartesian Genetic Programming in which a program is represented as an indexed graph. The graph is encoded in the form of a linear string of integers. The inputs or terminal set and node outputs are numbered sequentially. The node functions are also separately numbered. The genotype is just a list of node connections and functions. The genotype is then mapped to an indexed graph that can be executed as a program. Evolutionary algorithms are used to evolve the genotype in a symbolic regression problem (sixth order polynomial) and the Santa Fe Ant Trail. The computational effort is calculated for both cases. It is suggested that hit effort is a more reliable measure of computational efficiency. A neutral search strategy that allows the fittest genotype to be replaced by another equally fit genotype (a neutral genotype) is examined and compared with non-neutral search for the Santa Fe ant problem. The neutral search...

Parallel Distributed Genetic Programming (PDGP) is a new form of Genetic Programming (GP) suitable for the development of programs with a high degree of parallelism. Programs are represented in PDGP as graphs with nodes representing functions and terminals, and links representing the flow of control and results.

Parallel Distributed Genetic Programming (PDGP) is a new form of genetic programming suitable for the development of parallel programs in which symbolic and neural processing elements can be combined in a free and natural way. This paper describes the representation for programs and the genetic operators on which PDGP is based. Experimental results on the XOR problem are also reported. 1

Embedded Cartesian Genetic Programming (ECGP) is a form of the graph based Cartesian Genetic Programming (CGP) in which modules are automatically acquired and evolved. In this paper we compare the efficiencies of the ECGP and CGP techniques on three classes of problem: digital adders, digital multipliers and digital comparators. We show that in most cases ECGP shows a substantial improvement in performance over CGP and that the computational speedup is more pronounced on larger problems.

Abstract—The evolution of image filters using Genetic Programming is a relatively unexplored task. This is most likely due to the high computational cost of evaluating the evolved programs. We use the parallel processors available on modern graphics cards to greatly increase the speed of evaluation. Previous papers in this area dealt with noise reduction and edge detection. Here we demonstrate that other more complicated processes can also be successfully evolved, and that we can “reverse engineer ” the output from filters used in common graphics manipulation programs. I.

Shaping is a way in which a human designer can provide assistance to a learning system to enable it to solve problems that would otherwise defeat it. Results are presented showing that shaping can significantly improve the final performance of controllers evolved for a difficult visual tracking task. Controllers are developed in simulation and then transferred to a real robot head.

Genetic Programming was first introduced by Koza using tree representation together with a crossover technique in which random sub-branches of the parents ’ trees are swapped to create the offspring. Later Miller and Thomson introduced Cartesian Genetic Programming, which uses directed graphs as a representation to replace the tree structures originally introduced by Koza. Cartesian Genetic Programming has been shown to perform better than the traditional Genetic Programming; but it does not use crossover to create offspring, it is implemented using mutation only. In this paper a new crossover method in Genetic Programming is introduced. The new technique is based on an adaptation of the Cartesian Genetic Programming representation and is tested on two simple regression problems. It is shown that by implementing the new crossover technique, convergence is faster than that of using mutation only in the Cartesian Genetic Programming method.

Embedded Cartesian Genetic Programming (ECGP) is an extension of the directed graph based Cartesian Genetic Programming (CGP), which is capable of automatically acquiring, evolving and re-using partial solutions in the form of modules. In this paper, we apply for the first time, CGP and ECGP to the well known Lawnmower problem and to the Hierarchical-if-and-Only-if problem. The latter is normally associated with Genetic Algorithms. Computational effort figures are calculated from the results of both CGP and ECGP and our results compare favourably with other techniques.

Abstract. This paper describes Multiple Interactive Outputs in a Single Tree (MIOST), a new form of Genetic Programming (GP). Our approach is based on two ideas. Firstly, we have taken inspiration from graph-GP representations. With this idea we decided to explore the possibility of representing programs as graphs with oriented links. Secondly, our individuals could have more than one output. This idea was inspired on the divide and conquer principle, a program is decomposed in subprograms, and so, we are expecting to make the original problem easier by breaking down a problem into two or more sub-problems. To verify the effectiveness of our approach, we have used several evolvable hardware problems of different complexity taken from the literature. Our results indicate that our approach has a better overall performance in terms of consistency to reach feasible solutions.

Automated methods for designing robot controllers based on machine-learning techniques have shown great promise when applied to simple robot tasks, but in order to `scale up' to more complicated problems they will require assistance from human experts, a process that is often called `robot shaping'. In this paper, the difficult problem of learning how to visually track moving objects is examined. It is shown that through the use of shaping techniques, this intractable learning problem can be made soluble. Controllers are evolved in simulation and then transferred to a real robot.