Introduction Genetic programming is a domain-independent problem-solving approach in which computer programs are evolved to solve, or approximately solve, problems. Genetic programming is based on the Darwinian principle of reproduction and survival of the fittest and analogs of naturally occurring genetic operations such as crossover (sexual recombination) and mutation. John Holland's pioneering Adaptation in Natural and Artificial Systems (1975) described how an analog of the evolutionary process can be applied to solving mathematical problems and engineering optimization problems using what is now called the genetic algorithm (GA). The genetic algorithm attempts to find a good (or best) solution to the problem by genetically breeding a population of individuals over a series of generations. In the genetic algorithm, each individual in the population represents a candidate solut

In Genetic Programming, introns play at least two substantial roles: (1) A structural protection role, allowing the population to preserve highly-fit building blocks; and (2) A global protection role, enabling an individual to protect itself almost entirely against the destructive effect of crossover. We introduce Explicitly Defined Introns into Genetic Programming. Our results suggest that the introduction of Explicitly Defined Introns can improve fitness, generalization, and CPU time. Further, Explicitly Defined Introns partially replace the role of Implicit Introns ( that is, introns that emerge from crossover and mutation without being explicitly defined as such). Finally, Explicitly Defined Introns and Implicit Introns appear, in some situations, to work in tandem to produce better training, fitness and generalization than occurs without Explicitly Defined Introns. 1 Introduction Introns are an important part of the genomes of eucaryotic cells. In some genes, up to 70 %...

We apply linear genetic programming to several diagnosis problems in medicine. An efficient algorithm is presented that eliminates intron code in linear genetic programs. This results in a significant speedup which is especially interesting when operating with complex datasets as they are occuring in real-world applications like medicine. We compare our results to those obtained with neural networks and argue that genetic programming is able to show similar performance in classification and generalization even within a relatively small number of generations.

In this paper a new, general and exact schema theory for genetic programming is presented. The theory includes a microscopic schema theorem applicable to crossover operators which replace a subtree in one parent with a subtree from the other parent to produce the offspring. A more macroscopic schema theorem is also provided which is valid for crossover operators in which the probability of selecting any two crossover points in the parents depends only on their size and shape. The theory is based on the notions of Cartesian node reference systems and variable-arity hyperschemata both introduced here for the first time. In the paper we provide examples which show how the theory can be specialised to specific crossover operators and how it can be used to derive an exact definition of effective fitness and a size-evolution equation for GP. 1

We present a novel evolutionary approach to robotic control of a real robot based on genetic programming (GP). Our approach uses genetic programming techniques that manipulate machine code to evolve control programs for robots. This variant of GP has several advantages over a conventional GP system, such as higher speed, lower memory requirements and better real time properties. Previous attempts to apply GP in robotics use simulations to evaluate control programs and have difficulties with learning tasks involving a real robot. We present an on-line control method that is evaluated in two different physical environments and applied to two tasks using the Khepera robot platform: obstacle avoidance and object following. The results show fast learning and good generalization.

We investigate structural and semantic distance metrics for linear genetic programs. Causal

The importance of digital data compression in the future media arena cannot be overestimated. A novel approach to data compression is built on Genetic Programming. This technique has been referred to as "programmatic compression". In this paper we apply a variant of programmatic compression to the compression of bitmap images and sampled digital sound. The work presented here constitutes the first successful result of genetic programming applied to compression of real full size images and sound. A compiling genetic programming system is used for efficiency reasons. Different related issues are discussed, such as the handling of very large fitness case sets. 1 Introduction Programmatic compression is a very general form of compression. The basic idea behind this technique is that any system, which evolves programs or algorithms for generating data, can be viewed as a data compression system. The data that should be compressed are presented to the Genetic Programming system as fitness ...

We propose and study new search operators and a novel node representation that can make GP fitness landscapes smoother. Together with a tree evaluation method known as sub-machine code GP and the use of demes, these make up a recipe for solving very large parity problems using GP. We tested this recipe on parity problems with up to 22 input variables, solving them with a very high success probability.

Research into the utility of non--coding segments, or introns, in genetic--based encodings has shown that they expedite the evolution of solutions in domains by protecting building blocks against destructive crossover. We consider a genetic programming system where non--coding segments can be removed, and the resultant chromosomes returned into the population. This parsimonious repair leads to premature convergence, since as we remove the naturally occurring non--coding segments, we strip away their protective backup feature. We then duplicate the coding segments in the repaired chromosomes, and place the modified chromosomes into the population. The duplication method significantly improves the learning rate in the domain we have considered. We also show that this method can be applied to other domains. Introduction In genetic--based encodings (GBE), a bit is the atomic element of a chromosome and a non--coding segment is a continuous collection of bits ( 1) that do not contribute to...

Abstract not found