On linear genetic programming

The thesis is about linear genetic programming (LGP), a machine learning approach that evolves computer programs as sequences of imperative instructions. Two fundamental differences to the more commontree-based variant (TGP) may be identified. These are the graph-based functional structure of linear genetic programs, on the one hand, and the existence of structurally noneffective code, on the other hand.The two major objectives of this work comprise(1) the development of more advanced methods and variation operators to produce better and more compact program solutions and (2) the analysis of general EA/GP phenomena in linear GP, including intron code, neutral variations, and code growth, among others.First, we introduce efficient algorithms for extracting features of the imperative and functional structure of linear genetic programs.In doing so, especially the detection and elimination of noneffective code during runtime will turn out as a powerful tool to accelerate the time-consuming step of fitness evaluation in GP.Variation operators are discussed systematically for the linear program representation. We will demonstrate that so called effective instruction mutations achieve the best performance in terms of solution quality.These mutations operate only on the (structurally) effective codeand restrict the mutation step size to one instruction.One possibility to further improve their performance is to explicitly increase the probability of neutral variations. As a second, more time-efficient alternative we explicitly controlthe mutation step size on the effective code (effective step size).Minimum steps do not allow more than one effective instruction to change its effectiveness status. That is, only a single node may beconnected to or disconnected from the effective graph component. It is an interesting phenomenon that, to some extent, the effective code becomes more robust against destructions over the generations already implicitly. A special concern of this thesis is to convince the reader that thereare some serious arguments for using a linear representation.In a crossover-based comparison LGP has been found superior to TGPover a set of benchmark problems. Furthermore, linear solutions turned out to be more compact than tree solutions due to (1) multiple usage of subgraph results and (2) implicit parsimony pressure by structurally noneffective code.The phenomenon of code growth is analyzed for different lineargenetic operators. When applying instruction mutations exclusivelyalmost only neutral variations may be held responsible for the emergence and propagation of intron code. It is noteworthy that linear geneticprograms may not grow if all neutral variation effects are rejected and if the variation step size is minimum.For the same reasons effective instruction mutations realize an implicit complexity control in linear GP which reduces a possible negative effect of code growth to a minimum.Another noteworthy result in this context is that program size is strongly increased by crossover while it is hardly influenced by mutation even if step sizes are not explicitly restricted. Finally, we investigate program teams as one possibility to increasethe dimension of genetic programs. It will be demonstrated that muchmore powerful solutions may be found by teams than by individuals. Moreover, the complexity of team solutions remains surprisingly small compared to individual programs. Both is the result of specialization and cooperation of team members

Artificial evolution with Binary Decision Diagrams: a study in evolvability in neutral spaces

This thesis develops a new approach to evolving Binary Decision Diagrams, and uses it to study evolvability issues. For reasons that are not yet fully understood, current approaches to artificial evolution fail to exhibit the evolvability so readily exhibited in nature. To be able to apply evolvability to artificial evolution the field must first understand and characterise it; this will then lead to systems which are much more capable than they are currently. An experimental approach is taken. Carefully crafted, controlled experiments elucidate the mechanisms and properties that facilitate evolvability, focusing on the roles and interplay between neutrality, modularity, gradualism, robustness and diversity. Evolvability is found to emerge under gradual evolution as a biased distribution of functionality within the genotype-phenotype map, which serves to direct phenotypic variation. Neutrality facilitates fitness-conserving exploration, completely alleviating local optima. Population diversity, in conjunction with neutrality, is shown to facilitate the evolution of evolvability. The search is robust, scalable, and insensitive to the absence of initial diversity. The thesis concludes that gradual evolution in a search space that is free of local optima by way of neutrality can be a viable alternative to problematic evolution on multi-modal landscapes

Digital Ecosystems: Ecosystem-Oriented Architectures

We view Digital Ecosystems to be the digital counterparts of biological ecosystems. Here, we are concerned with the creation of these Digital Ecosystems, exploiting the self-organising properties of biological ecosystems to evolve high-level software applications. Therefore, we created the Digital Ecosystem, a novel optimisation technique inspired by biological ecosystems, where the optimisation works at two levels: a first optimisation, migration of agents which are distributed in a decentralised peer-to-peer network, operating continuously in time; this process feeds a second optimisation based on evolutionary computing that operates locally on single peers and is aimed at finding solutions to satisfy locally relevant constraints. The Digital Ecosystem was then measured experimentally through simulations, with measures originating from theoretical ecology, evaluating its likeness to biological ecosystems. This included its responsiveness to requests for applications from the user base, as a measure of the ecological succession (ecosystem maturity). Overall, we have advanced the understanding of Digital Ecosystems, creating Ecosystem-Oriented Architectures where the word ecosystem is more than just a metaphor.Comment: 39 pages, 26 figures, journa

An Empirical Study of Graph Grammar Evolution

Vukovar, Croati

Development of a hybrid genetic programming technique for computationally expensive optimisation problems

The increasing computational power of modern computers has contributed to the advance of nature-inspired algorithms in the fields of optimisation and metamodelling. Genetic programming (GP) is a genetically-inspired technique that can be used for metamodelling purposes. GP main strength is in the ability to infer the mathematical structure of the best model fitting a given data set, relying exclusively on input data and on a set of mathematical functions given by the user. Model inference is based on an iterative or evolutionary process, which returns the model as a symbolic expression (text expression). As a result, model evaluation is inexpensive and the generated expressions can be easily deployed to other users. Despite genetic programming has been used in many different branches of engineering, its diffusion on industrial scale is still limited. The aims of this thesis are to investigate the intrinsic limitations of genetic programming, to provide a comprehensive review of how researchers have tackled genetic programming main weaknesses and to improve genetic programming ability to extract accurate models from data. In particular, research has followed three main directions. The first has been the development of regularisation techniques to improve the generalisation ability of a model of a given mathematical structure, based on the use of a specific tuning algorithm in case sinusoidal functions are among the functions the model is composed of. The second has been the analysis of the influence that prior knowledge regarding the function to approximate may have on genetic programming inference process. The study has led to the introduction of a strategy that allows to use prior knowledge to improve model accuracy. Thirdly, the mathematical structure of the models returned by genetic programming has been systematically analysed and has led to the conclusion that the linear combination is the structure that is mostly returned by genetic programming runs. A strategy has been formulated to reduce the evolutionary advantage of linear combinations and to protect more complex classes of individuals throughout the evolution. The possibility to use genetic programming in industrial optimisation problems has also been assessed with the help of a new genetic programming implementation developed during the research activity. Such implementation is an open source project and is freely downloadable from http://www.personal.leeds.ac.uk/~cnua/mypage.html

Evolving Graphs by Graph Programming

Graphs are a ubiquitous data structure in computer science and can be used to represent solutions to difficult problems in many distinct domains. This motivates the use of Evolutionary Algorithms to search over graphs and efficiently find approximate solutions. However, existing techniques often represent and manipulate graphs in an ad-hoc manner. In contrast, rule-based graph programming offers a formal mechanism for describing relations over graphs. This thesis proposes the use of rule-based graph programming for representing and implementing genetic operators over graphs. We present the Evolutionary Algorithm Evolving Graphs by Graph Programming and a number of its extensions which are capable of learning stateful and stateless digital circuits, symbolic expressions and Artificial Neural Networks. We demonstrate that rule-based graph programming may be used to implement new and effective constraint-respecting mutation operators and show that these operators may strictly generalise others found in the literature. Through our proposal of Semantic Neutral Drift, we accelerate the search process by building plateaus into the fitness landscape using domain knowledge of equivalence. We also present Horizontal Gene Transfer, a mechanism whereby graphs may be passively recombined without disrupting their fitness. Through rigorous evaluation and analysis of over 20,000 independent executions of Evolutionary Algorithms, we establish numerous benefits of our approach. We find that on many problems, Evolving Graphs by Graph Programming and its variants may significantly outperform other approaches from the literature. Additionally, our empirical results provide further evidence that neutral drift aids the efficiency of evolutionary search